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Fu Y, Kenttämies A, Ruotsalainen S, Pirinen M, Tukiainen T. Role of X chromosome and dosage-compensation mechanisms in complex trait genetics. Am J Hum Genet 2025:S0002-9297(25)00145-4. [PMID: 40359939 DOI: 10.1016/j.ajhg.2025.04.004] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/09/2025] [Revised: 04/16/2025] [Accepted: 04/16/2025] [Indexed: 05/15/2025] Open
Abstract
The X chromosome (chrX) is often excluded from genome-wide association studies due to its unique biology complicating the analysis and interpretation of genetic data. Consequently, the influence of chrX on human complex traits remains debated. Here, we systematically assessed the relevance of chrX and the effect of its biology on complex traits by analyzing 48 quantitative traits in 343,695 individuals in UK Biobank with replication in 412,181 individuals from FinnGen. We show that, in the general population, chrX contributes to complex trait heritability at a rate of 3% of the autosomal heritability, consistent with the amount of genetic variation observed in chrX. We find that a pronounced male bias in chrX heritability supports the presence of near-complete dosage compensation between sexes through X chromosome inactivation (XCI). However, we also find subtle yet plausible evidence of escape from XCI contributing to human height. Assuming full XCI, the observed chrX contribution to complex trait heritability in both sexes is greater than expected given the presence of only a single active copy of chrX, mirroring potential dosage compensation between chrX and the autosomes. We find this enhanced contribution attributable to systematically larger active allele effects from chrX compared to autosomes in both sexes, independent of allele frequency and variant deleteriousness. Together, these findings support a model in which the two dosage-compensation mechanisms work in concert to balance the influence of chrX across the population while preserving sex-specific differences at a manageable level. Overall, our study advocates for more comprehensive locus discovery efforts in chrX.
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Affiliation(s)
- Yu Fu
- Institute for Molecular Medicine Finland (FIMM), Helsinki Institute of Life Science (HiLIFE), University of Helsinki, 00014 Helsinki, Finland
| | - Aino Kenttämies
- Institute for Molecular Medicine Finland (FIMM), Helsinki Institute of Life Science (HiLIFE), University of Helsinki, 00014 Helsinki, Finland
| | - Sanni Ruotsalainen
- Institute for Molecular Medicine Finland (FIMM), Helsinki Institute of Life Science (HiLIFE), University of Helsinki, 00014 Helsinki, Finland
| | - Matti Pirinen
- Institute for Molecular Medicine Finland (FIMM), Helsinki Institute of Life Science (HiLIFE), University of Helsinki, 00014 Helsinki, Finland; Department of Public Health, University of Helsinki, 00014 Helsinki, Finland; Department of Mathematics and Statistics, University of Helsinki, 00014 Helsinki, Finland
| | - Taru Tukiainen
- Institute for Molecular Medicine Finland (FIMM), Helsinki Institute of Life Science (HiLIFE), University of Helsinki, 00014 Helsinki, Finland.
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2
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Zhu Y, Chen W, Zhu K, Liu Y, Huang S, Zeng P. Polygenic prediction for underrepresented populations through transfer learning by utilizing genetic similarity shared with European populations. Brief Bioinform 2024; 26:bbaf048. [PMID: 39905953 PMCID: PMC11794457 DOI: 10.1093/bib/bbaf048] [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: 08/20/2024] [Revised: 01/10/2025] [Accepted: 01/21/2025] [Indexed: 02/06/2025] Open
Abstract
Because current genome-wide association studies are primarily conducted in individuals of European ancestry and information disparities exist among different populations, the polygenic score derived from Europeans thus exhibits poor transferability. Borrowing the idea of transfer learning, which enables the utilization of knowledge acquired from auxiliary samples to enhance learning capability in target samples, we propose transPGS, a novel polygenic score method, for genetic prediction in underrepresented populations by leveraging genetic similarity shared between the European and non-European populations while explaining the trans-ethnic difference in linkage disequilibrium (LD) and effect sizes. We demonstrate the usefulness and robustness of transPGS in elevated prediction accuracy via individual-level and summary-level simulations and apply it to seven continuous phenotypes and three diseases in the African, Chinese, and East Asian populations of the UK Biobank and Genetic Epidemiology Research Study on Adult Health and Aging cohorts. We further reveal that distinct LD and minor allele frequency patterns across ancestral groups are responsible for the dissatisfactory portability of PGS.
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Affiliation(s)
- Yiyang Zhu
- Department of Biostatistics, School of Public Health, Xuzhou Medical University, Xuzhou, Jiangsu, 221004, China
| | - Wenying Chen
- Department of Biostatistics, School of Public Health, Xuzhou Medical University, Xuzhou, Jiangsu, 221004, China
| | - Kexuan Zhu
- Department of Biostatistics, School of Public Health, Xuzhou Medical University, Xuzhou, Jiangsu, 221004, China
| | - Yuxin Liu
- Department of Biostatistics, School of Public Health, Xuzhou Medical University, Xuzhou, Jiangsu, 221004, China
| | - Shuiping Huang
- Department of Biostatistics, School of Public Health, Xuzhou Medical University, Xuzhou, Jiangsu, 221004, China
- Jiangsu Engineering Research Center of Biological Data Mining and Healthcare Transformation, Xuzhou Medical University, Xuzhou, Jiangsu, 221004, China
| | - Ping Zeng
- Department of Biostatistics, School of Public Health, Xuzhou Medical University, Xuzhou, Jiangsu, 221004, China
- Jiangsu Engineering Research Center of Biological Data Mining and Healthcare Transformation, Xuzhou Medical University, Xuzhou, Jiangsu, 221004, China
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3
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Freda PJ, Ghosh A, Bhandary P, Matsumoto N, Chitre AS, Zhou J, Hall MA, Palmer AA, Obafemi-Ajayi T, Moore JH. PAGER: A novel genotype encoding strategy for modeling deviations from additivity in complex trait association studies. BioData Min 2024; 17:41. [PMID: 39394173 PMCID: PMC11468469 DOI: 10.1186/s13040-024-00393-x] [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: 06/07/2024] [Accepted: 09/30/2024] [Indexed: 10/13/2024] Open
Abstract
BACKGROUND The additive model of inheritance assumes that heterozygotes (Aa) are exactly intermediate in respect to homozygotes (AA and aa). While this model is commonly used in single-locus genetic association studies, significant deviations from additivity are well-documented and contribute to phenotypic variance across many traits and systems. This assumption can introduce type I and type II errors by overestimating or underestimating the effects of variants that deviate from additivity. Alternative genotype encoding strategies have been explored to account for different inheritance patterns, but they often incur significant computational or methodological costs. To address these challenges, we introduce PAGER (Phenotype Adjusted Genotype Encoding and Ranking), an efficient pre-processing method that encodes each genetic variant based on normalized mean phenotypic differences between diallelic genotype classes (AA, Aa, and aa). This approach more accurately reflects each variant's true inheritance model, improving model precision while minimizing the costs associated with alternative encoding strategies. RESULTS Through extensive benchmarking on SNPs simulated with both binary and continuous phenotypes, we demonstrate that PAGER accurately represents various inheritance patterns (including additive, dominant, recessive, and heterosis), achieves levels of statistical power that meet or exceed other encoding strategies, and attains computation speeds up to 55 times faster than a similar method, EDGE. We also apply PAGER to publicly available real-world data and identify a novel, relevant putative QTL associated with body mass index in rats (Rattus norvegicus) that is not detected with the additive model. CONCLUSIONS Overall, we show that PAGER is an efficient genotype encoding approach that can uncover sources of missing heritability and reveal novel insights in the study of complex traits while incurring minimal costs.
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Affiliation(s)
- Philip J Freda
- Department of Computational Biomedicine, Cedars-Sinai Medical Center, 700 N. San Vincente Blvd., Pacific Design Center, Suite G540, West Hollywood, CA, 90069, USA
| | - Attri Ghosh
- Department of Computational Biomedicine, Cedars-Sinai Medical Center, 700 N. San Vincente Blvd., Pacific Design Center, Suite G540, West Hollywood, CA, 90069, USA
| | - Priyanka Bhandary
- Department of Computational Biomedicine, Cedars-Sinai Medical Center, 700 N. San Vincente Blvd., Pacific Design Center, Suite G540, West Hollywood, CA, 90069, USA
| | - Nicholas Matsumoto
- Department of Computational Biomedicine, Cedars-Sinai Medical Center, 700 N. San Vincente Blvd., Pacific Design Center, Suite G540, West Hollywood, CA, 90069, USA
| | - Apurva S Chitre
- Department of Psychiatry, University of California San Diego, 9500 Gilman Dr, La Jolla, CA, 92093-0667, USA
| | - Jiayan Zhou
- Department of Medicine, Stanford University School of Medicine, 291 Campus Dr., Li Ka Shing Building, Stanford, CA, 94305, USA
| | - Molly A Hall
- Department of Genetics and Institute for Biomedical Informatics, University of Pennsylvania, 3700 Hamilton Walk, Richards Building A301, Philadelphia, PA, 19104, USA
| | - Abraham A Palmer
- Department of Psychiatry, University of California San Diego, 9500 Gilman Dr, La Jolla, CA, 92093-0667, USA
- Institute for Genomic Medicine, University of California San Diego, 9500 Gilman Dr, La Jolla, CA, 92093-0667, USA
| | - Tayo Obafemi-Ajayi
- Cooperative Engineering Program, Missouri State University, 901 S. National Ave, Springfield, MO, 65897, USA
| | - Jason H Moore
- Department of Computational Biomedicine, Cedars-Sinai Medical Center, 700 N. San Vincente Blvd., Pacific Design Center, Suite G540, West Hollywood, CA, 90069, USA.
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Freda PJ, Ye S, Zhang R, Moore JH, Urbanowicz RJ. Assessing the limitations of relief-based algorithms in detecting higher-order interactions. BioData Min 2024; 17:37. [PMID: 39354639 PMCID: PMC11443793 DOI: 10.1186/s13040-024-00390-0] [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: 08/06/2024] [Accepted: 09/04/2024] [Indexed: 10/03/2024] Open
Abstract
BACKGROUND Epistasis, the interaction between genetic loci where the effect of one locus is influenced by one or more other loci, plays a crucial role in the genetic architecture of complex traits. However, as the number of loci considered increases, the investigation of epistasis becomes exponentially more complex, making the selection of key features vital for effective downstream analyses. Relief-Based Algorithms (RBAs) are often employed for this purpose due to their reputation as "interaction-sensitive" algorithms and uniquely non-exhaustive approach. However, the limitations of RBAs in detecting interactions, particularly those involving multiple loci, have not been thoroughly defined. This study seeks to address this gap by evaluating the efficiency of RBAs in detecting higher-order epistatic interactions. Motivated by previous findings that suggest some RBAs may rank predictive features involved in higher-order epistasis negatively, we explore the potential of absolute value ranking of RBA feature weights as an alternative approach for capturing complex interactions. In this study, we assess the performance of ReliefF, MultiSURF, and MultiSURFstar on simulated genetic datasets that model various patterns of genotype-phenotype associations, including 2-way to 5-way genetic interactions, and compare their performance to two control methods: a random shuffle and mutual information. RESULTS Our findings indicate that while RBAs effectively identify lower-order (2 to 3-way) interactions, their capability to detect higher-order interactions is significantly limited, primarily by large feature count but also by signal noise. Specifically, we observe that RBAs are successful in detecting fully penetrant 4-way XOR interactions using an absolute value ranking approach, but this is restricted to datasets with only 20 total features. CONCLUSIONS These results highlight the inherent limitations of current RBAs and underscore the need for the development of Relief-based approaches with enhanced detection capabilities for the investigation of epistasis, particularly in datasets with large feature counts and complex higher-order interactions.
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Affiliation(s)
- Philip J Freda
- Computational Biomedicine, Cedars-Sinai Medical Center, 700 N. San Vicente Blvd., Pacific Design Center, Suite G540, West Hollywood, 90069, CA, USA
| | - Suyu Ye
- Whiting School of Engineering, Johns Hopkins University, 3400 N. Charles St., Baltimore, 21218, MD, USA
| | - Robert Zhang
- University of Pennsylvania, Philadelphia, 19104, PA, USA
| | - Jason H Moore
- Computational Biomedicine, Cedars-Sinai Medical Center, 700 N. San Vicente Blvd., Pacific Design Center, Suite G540, West Hollywood, 90069, CA, USA
| | - Ryan J Urbanowicz
- Computational Biomedicine, Cedars-Sinai Medical Center, 700 N. San Vicente Blvd., Pacific Design Center, Suite G540, West Hollywood, 90069, CA, USA.
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Cho HW, Jin HS, Kim SS, Eom YB. Forensic height estimation using polygenic score in Korean population. Mol Genet Genomics 2024; 299:78. [PMID: 39120737 DOI: 10.1007/s00438-024-02172-z] [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: 07/31/2023] [Accepted: 07/30/2024] [Indexed: 08/10/2024]
Abstract
Height is known to be a classically heritable trait controlled by complex polygenic factors. Numerous height-associated genetic variants across the genome have been identified so far. It is also a representative of externally visible characteristics (EVC) for predicting appearance in forensic science. When biological evidence at a crime scene is deficient in identifying an individual, the examination of forensic DNA phenotyping using some genetic variants could be considered. In this study, we aimed to predict 'height', a representative forensic phenotype, by using a small number of genetic variants when short tandem repeat (STR) analysis is hard with insufficient biological samples. Our results not only replicated previous genetic signals but also indicated an upward trend in polygenic score (PGS) with increasing height in the validation and replication stages for both genders. These results demonstrate that the established SNP sets in this study could be used for height estimation in the Korean population. Specifically, since the PGS model constructed in this study targets only a small number of SNPs, it contributes to enabling forensic DNA phenotyping even at crime scenes with a minimal amount of biological evidence. To the best of our knowledge, this was the first study to evaluate a PGS model for height estimation in the Korean population using GWAS signals. Our study offers insight into the polygenic effect of height in East Asians, incorporating genetic variants from non-Asian populations.
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Affiliation(s)
- Hye-Won Cho
- Department of Medical Sciences, Graduate School, Soonchunhyang University, Asan, 31538, Chungnam, Republic of Korea
| | - Hyun-Seok Jin
- Department of Biomedical Laboratory Science, College of Life and Health Sciences, Hoseo University, Asan, 31499, Chungnam, Republic of Korea
| | - Sung-Soo Kim
- Department of Biomedical Laboratory Science, College of Life and Health Sciences, Hoseo University, Asan, 31499, Chungnam, Republic of Korea
| | - Yong-Bin Eom
- Department of Medical Sciences, Graduate School, Soonchunhyang University, Asan, 31538, Chungnam, Republic of Korea.
- Department of Biomedical Laboratory Science, College of Medical Sciences, Soonchunhyang University, 22 Soonchunhyang-ro, Sinchang-myeon, Asan-si, 31538, Chungcheongnam-do, Republic of Korea.
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Topa H, Benoit-Pilven C, Tukiainen T, Pietiläinen O. X-chromosome inactivation in human iPSCs provides insight into X-regulated gene expression in autosomes. Genome Biol 2024; 25:144. [PMID: 38822397 PMCID: PMC11143737 DOI: 10.1186/s13059-024-03286-8] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/17/2023] [Accepted: 05/17/2024] [Indexed: 06/03/2024] Open
Abstract
BACKGROUND Variation in X chromosome inactivation (XCI) in human-induced pluripotent stem cells (hiPSCs) can impact their ability to model biological sex biases. The gene-wise landscape of X chromosome gene dosage remains unresolved in female hiPSCs. To characterize patterns of de-repression and escape from inactivation, we performed a systematic survey of allele specific expression in 165 female hiPSC lines. RESULTS XCI erosion is non-random and primarily affects genes that escape XCI in human tissues. Individual genes and cell lines vary in the frequency and degree of de-repression. Bi-allelic expression increases gradually after modest decrease of XIST in cultures, whose loss is commonly used to mark lines with eroded XCI. We identify three clusters of female lines at different stages of XCI. Increased XCI erosion amplifies female-biased expression at hypomethylated sites and regions normally occupied by repressive histone marks, lowering male-biased differences in the X chromosome. In autosomes, erosion modifies sex differences in a dose-dependent way. Male-biased genes are enriched for hypermethylated regions, and de-repression of XIST-bound autosomal genes in female lines attenuates normal male-biased gene expression in eroded lines. XCI erosion can compensate for a dominant loss of function effect in several disease genes. CONCLUSIONS We present a comprehensive view of X chromosome gene dosage in hiPSCs and implicate a direct mechanism for XCI erosion in regulating autosomal gene expression in trans. The uncommon and variable reactivation of X chromosome genes in female hiPSCs can provide insight into X chromosome's role in regulating gene expression and sex differences in humans.
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Affiliation(s)
- Hande Topa
- Neuroscience Center, Helsinki Institute of Life Science, University of Helsinki, Helsinki, Finland
| | - Clara Benoit-Pilven
- Institute for Molecular Medicine Finland, Helsinki Institute of Life Science, University of Helsinki, Helsinki, Finland
| | - Taru Tukiainen
- Institute for Molecular Medicine Finland, Helsinki Institute of Life Science, University of Helsinki, Helsinki, Finland
| | - Olli Pietiläinen
- Neuroscience Center, Helsinki Institute of Life Science, University of Helsinki, Helsinki, Finland.
- The Stanley Center for Psychiatric Research at the Broad Institute, of MIT and Harvard, Cambridge, MA, USA.
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7
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Xu X, Xu X, Zakeri MA, Wang SY, Yan M, Wang YH, Li L, Sun ZL, Wang RY, Miao LZ. Assessment of causal relationships between omega-3 and omega-6 polyunsaturated fatty acids in autoimmune rheumatic diseases: a brief research report from a Mendelian randomization study. Front Nutr 2024; 11:1356207. [PMID: 38863588 PMCID: PMC11165037 DOI: 10.3389/fnut.2024.1356207] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/15/2023] [Accepted: 05/08/2024] [Indexed: 06/13/2024] Open
Abstract
BACKGROUND Currently, the association between the consumption of polyunsaturated fatty acids (PUFAs) and the susceptibility to autoimmune rheumatic diseases (ARDs) remains conflict and lacks substantial evidence in various clinical studies. To address this issue, we employed Mendelian randomization (MR) to establish causal links between six types of PUFAs and their connection to the risk of ARDs. METHODS We retrieved summary-level data on six types of PUFAs, and five different types of ARDs from publicly accessible GWAS statistics. Causal relationships were determined using a two-sample MR analysis, with the IVW approach serving as the primary analysis method. To ensure the reliability of our research findings, we used four complementary approaches and conducted multivariable MR analysis (MVMR). Additionally, we investigated reverse causality through a reverse MR analysis. RESULTS Our results indicate that a heightened genetic predisposition for elevated levels of EPA (ORIVW: 0.924, 95% CI: 0.666-1.283, P IVW = 0.025) was linked to a decreased susceptibility to psoriatic arthritis (PsA). Importantly, the genetically predicted higher levels of EPA remain significantly associated with an reduced risk of PsA, even after adjusting for multiple testing using the FDR method (P IVW-FDR-corrected = 0.033) and multivariable MR analysis (P MV-IVW < 0.05), indicating that EPA may be considered as the risk-protecting PUFAs for PsA. Additionally, high levels of LA showed a positive causal relationship with a higher risk of PsA (ORIVW: 1.248, 95% CI: 1.013-1.538, P IVW = 0.037). It is interesting to note, however, that the effects of these associations were weakened in our MVMR analyses, which incorporated adjustment for lipid profiles (P MV-IVW > 0.05) and multiple testing using the FDR method (P IVW-FDR-corrected = 0.062). Moreover, effects of total omega-3 PUFAs, DHA, EPA, and LA on PsA, were massively driven by SNP effects in the FADS gene region. Furthermore, no causal association was identified between the concentrations of other circulating PUFAs and the risk of other ARDs. Further analysis revealed no significant horizontal pleiotropy and heterogeneity or reverse causality. CONCLUSION Our comprehensive MR analysis indicated that EPA is a key omega-3 PUFA that may protect against PsA but not other ARDs. The FADS2 gene appears to play a central role in mediating the effects of omega-3 PUFAs on PsA risk. These findings suggest that EPA supplementation may be a promising strategy for preventing PsA onset. Further well-powered epidemiological studies and clinical trials are warranted to explore the potential mechanisms underlying the protective effects of EPA in PsA.
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Affiliation(s)
- Xiao Xu
- School of Nursing, Nantong Health College of Jiangsu Province, Nantong, China
| | - Xu Xu
- Department of Geriatrics, Renmin Hospital of Wuhan University, Wuhan, China
| | - Mohammad Ali Zakeri
- Non-Communicable Diseases Research Center, Rafsanjan University of Medical Sciences, Rafsanjan, Iran
| | - Shu-Yun Wang
- Department of Postgraduate, St. Paul University Philippines, Tuggegarau, Philippines
| | - Min Yan
- Department of Epidemiology, School of Public Health, Changzhou University, Changzhou, China
- Faculty of Health and Welfare, Satakunta University of Applied Sciences, Pori, Finland
| | - Yuan-Hong Wang
- Department of Rheumatology, The Affiliated Hospital of Nanjing University of Chinese Medicine, Nanjing, China
| | - Li Li
- Department of Rheumatology, The Affiliated Hospital of Nanjing University of Chinese Medicine, Nanjing, China
| | - Zhi-ling Sun
- Department of Epidemiology, School of Public Health, Nanjing University of Chinese Medicine, Nanjing, China
| | - Rong-Yun Wang
- Department of Rheumatology, Zhejiang Chinese Medical University, Hangzhou, China
| | - Lin-Zhong Miao
- Department of Nursing, Children’s Hospital of Soochow University, Soochow University, Suzhou, China
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8
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Renaudineau Y, Charras A, Natoli V, Fusaro M, Smith EMD, Beresford MW, Hedrich CM. Type I interferon associated epistasis may contribute to early disease-onset and high disease activity in juvenile-onset lupus. Clin Immunol 2024; 262:110194. [PMID: 38508295 DOI: 10.1016/j.clim.2024.110194] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/04/2023] [Revised: 03/01/2024] [Accepted: 03/10/2024] [Indexed: 03/22/2024]
Abstract
Pathologic type I interferon (T1IFN) expression is a key feature in systemic lupus erythematosus (SLE) that associates with disease activity. When compared to adult-onset disease, juvenile-onset (j)SLE is characterized by increased disease activity and damage, which likely relates to increased genetic burden. To identify T1IFN-associated gene polymorphisms (TLR7, IRAK1, miR-3142/miR-146a, IRF5, IRF7, IFIH1, IRF8, TYK2, STAT4), identify long-range linkage disequilibrium and gene:gene interrelations, 319 jSLE patients were genotyped using panel sequencing. Coupling phenotypic quantitative trait loci (QTL) analysis identified 10 jSLE QTL that associated with young age at onset (<12 years; IRAK1 [rs1059702], TLR7 [rs3853839], IFIH1 [rs11891191, rs1990760, rs3747517], STAT4 [rs3021866], TYK2 [rs280501], IRF8 [rs1568391, rs6638]), global disease activity (SLEDAI-2 K >10; IFIH1 [rs1990760], STAT4 [rs3021866], IRF8 [rs903202, rs1568391, rs6638]), and mucocutaneous involvement (TLR7 [rs3853839], IFIH1 [rs11891191, rs1990760]). This study suggests T1IFN-associated polymorphisms and gene:gene interrelations in jSLE. Genotyping of jSLE patients may allow for individualized treatment and care.
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Affiliation(s)
- Yves Renaudineau
- Immunology Department Laboratory, Referral Medical Biology Laboratory, Institut Fédératif de Biologie, Toulouse University Hospital Center, France; INFINITy, Toulouse Institute for Infectious and Inflammatory Diseases, INSERM U1291, CNRS U5051, University Toulouse III, Toulouse, France
| | - Amandine Charras
- Department of Women's & Children's Health, Institute of Life Course and Medical Sciences, University of Liverpool, UK
| | - Valentina Natoli
- Department of Women's & Children's Health, Institute of Life Course and Medical Sciences, University of Liverpool, UK; Università degli Studi di Genova, Dipartimento di Neuroscienze, riabilitazione, oftalmologia, genetica e scienze materno-infantili, DINOGMI, Genoa, Italy
| | - Mathieu Fusaro
- Immunology Department Laboratory, Referral Medical Biology Laboratory, Institut Fédératif de Biologie, Toulouse University Hospital Center, France; INFINITy, Toulouse Institute for Infectious and Inflammatory Diseases, INSERM U1291, CNRS U5051, University Toulouse III, Toulouse, France
| | - Eve M D Smith
- Department of Women's & Children's Health, Institute of Life Course and Medical Sciences, University of Liverpool, UK; Department of Rheumatology, Alder Hey Children's NHS Foundation Trust, Liverpool, UK
| | - Michael W Beresford
- Department of Women's & Children's Health, Institute of Life Course and Medical Sciences, University of Liverpool, UK; Department of Rheumatology, Alder Hey Children's NHS Foundation Trust, Liverpool, UK
| | - Christian M Hedrich
- Department of Women's & Children's Health, Institute of Life Course and Medical Sciences, University of Liverpool, UK; Department of Rheumatology, Alder Hey Children's NHS Foundation Trust, Liverpool, UK.
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9
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Hunter SK, S Angadi S, Bhargava A, Harper J, Hirschberg AL, D Levine B, L Moreau K, J Nokoff N, Stachenfeld NS, Bermon S. The Biological Basis of Sex Differences in Athletic Performance: Consensus Statement for the American College of Sports Medicine. Med Sci Sports Exerc 2023; 55:2328-2360. [PMID: 37772882 DOI: 10.1249/mss.0000000000003300] [Citation(s) in RCA: 49] [Impact Index Per Article: 24.5] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 09/30/2023]
Abstract
ABSTRACT Biological sex is a primary determinant of athletic performance because of fundamental sex differences in anatomy and physiology dictated by sex chromosomes and sex hormones. Adult men are typically stronger, more powerful, and faster than women of similar age and training status. Thus, for athletic events and sports relying on endurance, muscle strength, speed, and power, males typically outperform females by 10%-30% depending on the requirements of the event. These sex differences in performance emerge with the onset of puberty and coincide with the increase in endogenous sex steroid hormones, in particular testosterone in males, which increases 30-fold by adulthood, but remains low in females. The primary goal of this consensus statement is to provide the latest scientific knowledge and mechanisms for the sex differences in athletic performance. This review highlights the differences in anatomy and physiology between males and females that are primary determinants of the sex differences in athletic performance and in response to exercise training, and the role of sex steroid hormones (particularly testosterone and estradiol). We also identify historical and nonphysiological factors that influence the sex differences in performance. Finally, we identify gaps in the knowledge of sex differences in athletic performance and the underlying mechanisms, providing substantial opportunities for high-impact studies. A major step toward closing the knowledge gap is to include more and equitable numbers of women to that of men in mechanistic studies that determine any of the sex differences in response to an acute bout of exercise, exercise training, and athletic performance.
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Affiliation(s)
- Sandra K Hunter
- Exercise Science Program, Department of Physical Therapy, and Athletic and Human Performance Center, Marquette University, Milwaukee, WI
| | | | - Aditi Bhargava
- Department of Obstetrics and Gynecology, Center for Reproductive Sciences, University of California, San Francisco, CA
| | - Joanna Harper
- Loughborough University, Loughborough, UNITED KINGDOM
| | - Angelica Lindén Hirschberg
- Department of Women's and Children's Health, Karolinska Institutet, and Department of Gynecology and Reproductive Medicine, Karolinska University Hospital, Stockholm, SWEDEN
| | - Benjamin D Levine
- Institute for Exercise and Environmental Medicine, Texas Health Presbyterian Hospital Dallas, and the Department of Internal Medicine, Division of Cardiology, University of Texas Southwestern Medical Center, Dallas, TX
| | - Kerrie L Moreau
- Department of Medicine, Division of Geriatric Medicine, University of Colorado Anschutz Medical Campus, and Eastern Colorado Health Care System, Geriatric Research Education and Clinical Center, Aurora, CO
| | - Natalie J Nokoff
- Department of Pediatrics, Section of Endocrinology, University of Colorado Anschutz Medical Campus, Aurora, CO
| | - Nina S Stachenfeld
- The John B. Pierce Laboratory and Department of Obstetrics, Gynecology and Reproductive Sciences, Yale School of Medicine, New Haven, CT
| | - Stéphane Bermon
- Health and Science Department, World Athletics, Monaco and the LAMHESS, University Côte d'Azur, Nice, FRANCE
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10
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Tabassum R, Widén E, Ripatti S. Effect of biological sex on human circulating lipidome: An overview of the literature. Atherosclerosis 2023; 384:117274. [PMID: 37743161 DOI: 10.1016/j.atherosclerosis.2023.117274] [Citation(s) in RCA: 8] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 02/28/2023] [Revised: 04/28/2023] [Accepted: 09/01/2023] [Indexed: 09/26/2023]
Abstract
Cardiovascular diseases (CVD) are the leading cause of death worldwide for both men and women, but their prevalence and burden show marked sex differences. The existing knowledge gaps in research, prevention, and treatment for women emphasize the need for understanding the biological mechanisms contributing to the sex differences in CVD. Sex differences in the plasma lipids that are well-known risk factors and predictors of CVD events have been recognized and are believed to contribute to the known disparities in CVD manifestations in men and women. However, the current understanding of sex differences in lipids has mainly come from the studies on routinely measured standard lipids- low-density lipoprotein cholesterol (LDL-C), high-density lipoprotein cholesterol (HDL-C), total triglycerides, and total cholesterol, which have been the mainstay of the lipid profiling. Sex differences in individual lipid species, collectively called the lipidome, have until recently been less explored due to the technological challenges and analytic costs. With the technological advancements in the last decade and growing interest in understanding mechanisms of sexual dimorphism in metabolic disorders, many investigators utilized metabolomics and lipidomics based platforms to examine the effect of biological sex on detailed lipidomic profiles and individual lipid species. This review presents an overview of the research on sex differences in the concentrations of circulating lipid species, focusing on findings from the metabolome- and lipidome-wide studies. We also discuss the potential contribution of genetic factors including sex chromosomes and sex-specific physiological factors such as menopause and sex hormones to the sex differences in lipidomic profiles.
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Affiliation(s)
- Rubina Tabassum
- Institute for Molecular Medicine Finland, HiLIFE, University of Helsinki, Helsinki, Finland.
| | - Elisabeth Widén
- Institute for Molecular Medicine Finland, HiLIFE, University of Helsinki, Helsinki, Finland
| | - Samuli Ripatti
- Institute for Molecular Medicine Finland, HiLIFE, University of Helsinki, Helsinki, Finland; Department of Public Health, Faculty of Medicine, University of Helsinki, Helsinki, Finland; Broad Institute of the Massachusetts Institute of Technology and Harvard University, Cambridge, MA, USA.
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11
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Cho WK, Baek I, Kim SE, Kim M, Kim T, Suh B. Association of ITM2A rs1751094 polymorphism on X chromosome in Korean pediatric patients with autoimmune thyroid disease. Immun Inflamm Dis 2023; 11:e800. [PMID: 36988246 PMCID: PMC10013136 DOI: 10.1002/iid3.800] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/25/2022] [Revised: 02/08/2023] [Accepted: 02/21/2023] [Indexed: 03/17/2023] Open
Abstract
BACKGROUND Autoimmune thyroid disease (AITD) manifests with a female predominance, and much attention has been directed towards the integral membrane protein 2 A (ITM2A) gene located on the X chromosome. METHODS In a study of 166 pediatric patients with autoimmune thyroid disease (AITD), the ITM2A rs1751094 single-nucleotide polymorphism (SNP) was genotyped. The sample comprised 143 females and 23 males, with 67 patients diagnosed with Hashimoto chronic thyroiditis (HD) and 99 with Graves' disease (GD). In the 99 GD patients, 49 (49.5%) exhibited thyroid-associated ophthalmopathy (TAO). Among the 85 GD patients, 70.6% (60/85) were considered intractable GD. The results were compared to those from 198 healthy Korean individuals, including 97 females and 101 males. RESULTS The frequency of the rs1751094 C allele and CC/AC genotype were higher in AITD, GD and HD patients compared to controls, while the frequency of the A allele and AA genotype were lower. The results were more pronounced in female AITD and GD patients compared to male patients. The association was also found in intractable GD and TAO patients. Target SNP fits Hardy-Weinberg equilibrium. CONCLUSIONS These findings indicate that the ITM2A gene polymorphism on the X chromosome may contribute to the immunological basis of female-predominant AITD in Korean children.
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Affiliation(s)
- Won K. Cho
- Department of Pediatrics, College of Medicine, St. Vincent's HospitalThe Catholic University of KoreaSeoulKorea
| | - In‐Cheol Baek
- Catholic Hematopoietic Stem Cell Bank, College of MedicineThe Catholic University of KoreaSeoulKorea
| | - Sung E. Kim
- Department of Pediatrics, Incheon St. Mary's Hospital, College of MedicineThe Catholic University of KoreaSeoulKorea
| | - Mirae Kim
- Catholic Hematopoietic Stem Cell Bank, College of MedicineThe Catholic University of KoreaSeoulKorea
| | - Tai‐Gyu Kim
- Catholic Hematopoietic Stem Cell Bank, College of MedicineThe Catholic University of KoreaSeoulKorea
- Department of Microbiology, College of MedicineThe Catholic University of KoreaSeoulKorea
| | - Byung‐Kyu Suh
- Department of Pediatrics, College of Medicine, Seoul St. Mary's HospitalThe Catholic University of KoreaSeoulKorea
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12
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Zhao Z, Fritsche LG, Smith JA, Mukherjee B, Lee S. The construction of cross-population polygenic risk scores using transfer learning. Am J Hum Genet 2022; 109:1998-2008. [PMID: 36240765 PMCID: PMC9674947 DOI: 10.1016/j.ajhg.2022.09.010] [Citation(s) in RCA: 39] [Impact Index Per Article: 13.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/12/2022] [Accepted: 09/22/2022] [Indexed: 01/26/2023] Open
Abstract
As most existing genome-wide association studies (GWASs) were conducted in European-ancestry cohorts, and as the existing polygenic risk score (PRS) models have limited transferability across ancestry groups, PRS research on non-European-ancestry groups needs to make efficient use of available data until we attain large sample sizes across all ancestry groups. Here we propose a PRS method using transfer learning techniques. Our approach, TL-PRS, uses gradient descent to fine-tune the baseline PRS model from an ancestry group with large sample GWASs to the dataset of target ancestry. In our application of constructing PRS for seven quantitative and two dichotomous traits for 10,285 individuals of South Asian ancestry and 8,168 individuals of African ancestry in UK Biobank, TL-PRS using PRS-CS as a baseline method obtained 25% average relative improvement for South Asian samples and 29% for African samples compared to the standard PRS-CS method in terms of predicted R2. Our approach increases the transferability of PRSs across ancestries and thereby helps reduce existing inequities in genetics research.
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Affiliation(s)
- Zhangchen Zhao
- Department of Biostatistics, University of Michigan School of Public Health, Ann Arbor, MI 48109, USA,Corresponding author
| | - Lars G. Fritsche
- Department of Biostatistics, University of Michigan School of Public Health, Ann Arbor, MI 48109, USA,Rogel Cancer Center, University of Michigan Medicine, Ann Arbor, MI 48109, USA
| | - Jennifer A. Smith
- Department of Epidemiology, University of Michigan School of Public Health, Ann Arbor, MI 48109, USA
| | - Bhramar Mukherjee
- Department of Biostatistics, University of Michigan School of Public Health, Ann Arbor, MI 48109, USA,Rogel Cancer Center, University of Michigan Medicine, Ann Arbor, MI 48109, USA,Department of Epidemiology, University of Michigan School of Public Health, Ann Arbor, MI 48109, USA,Corresponding author
| | - Seunggeun Lee
- Department of Biostatistics, University of Michigan School of Public Health, Ann Arbor, MI 48109, USA,Graduate School of Data Science, Seoul National University, Seoul, South Korea,Corresponding author
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13
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Tian P, Chan TH, Wang YF, Yang W, Yin G, Zhang YD. Multiethnic polygenic risk prediction in diverse populations through transfer learning. Front Genet 2022; 13:906965. [PMID: 36061179 PMCID: PMC9438789 DOI: 10.3389/fgene.2022.906965] [Citation(s) in RCA: 19] [Impact Index Per Article: 6.3] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/29/2022] [Accepted: 06/27/2022] [Indexed: 11/28/2022] Open
Abstract
Polygenic risk scores (PRS) leverage the genetic contribution of an individual's genotype to a complex trait by estimating disease risk. Traditional PRS prediction methods are predominantly for the European population. The accuracy of PRS prediction in non-European populations is diminished due to much smaller sample size of genome-wide association studies (GWAS). In this article, we introduced a novel method to construct PRS for non-European populations, abbreviated as TL-Multi, by conducting a transfer learning framework to learn useful knowledge from the European population to correct the bias for non-European populations. We considered non-European GWAS data as the target data and European GWAS data as the informative auxiliary data. TL-Multi borrows useful information from the auxiliary data to improve the learning accuracy of the target data while preserving the efficiency and accuracy. To demonstrate the practical applicability of the proposed method, we applied TL-Multi to predict the risk of systemic lupus erythematosus (SLE) in the Asian population and the risk of asthma in the Indian population by borrowing information from the European population. TL-Multi achieved better prediction accuracy than the competing methods, including Lassosum and meta-analysis in both simulations and real applications.
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Affiliation(s)
- Peixin Tian
- Department of Statistics and Actuarial Science, The University of Hong Kong, Hong Kong SAR, China
| | - Tsai Hor Chan
- Department of Statistics and Actuarial Science, The University of Hong Kong, Hong Kong SAR, China
| | - Yong-Fei Wang
- Department of Paediatrics and Adolescent Medicine, The University of Hong Kong, Hong Kong SAR, China
| | - Wanling Yang
- Department of Paediatrics and Adolescent Medicine, The University of Hong Kong, Hong Kong SAR, China
| | - Guosheng Yin
- Department of Statistics and Actuarial Science, The University of Hong Kong, Hong Kong SAR, China
| | - Yan Dora Zhang
- Department of Statistics and Actuarial Science, The University of Hong Kong, Hong Kong SAR, China
- Centre for PanorOmic Sciences, Li Ka Shing Faculty of Medicine, The University of Hong Kong, Hong Kong SAR, China
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14
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Dwaib HS, AlZaim I, Ajouz G, Eid AH, El-Yazbi A. Sex Differences in Cardiovascular Impact of Early Metabolic Impairment: Interplay between Dysbiosis and Adipose Inflammation. Mol Pharmacol 2022; 102:481-500. [PMID: 34732528 DOI: 10.1124/molpharm.121.000338] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/14/2021] [Accepted: 10/23/2021] [Indexed: 11/22/2022] Open
Abstract
The evolving view of gut microbiota has shifted toward describing the colonic flora as a dynamic organ in continuous interaction with systemic physiologic processes. Alterations of the normal gut bacterial profile, known as dysbiosis, has been linked to a wide array of pathologies. Of particular interest is the cardiovascular-metabolic disease continuum originating from positive energy intake and high-fat diets. Accumulating evidence suggests a role for sex hormones in modulating the gut microbiome community. Such a role provides an additional layer of modulation of the early inflammatory changes culminating in negative metabolic and cardiovascular outcomes. In this review, we will shed the light on the role of sex hormones in cardiovascular dysfunction mediated by high-fat diet-induced dysbiosis, together with the possible involvement of insulin resistance and adipose tissue inflammation. Insights into novel therapeutic interventions will be discussed as well. SIGNIFICANCE STATEMENT: Increasing evidence implicates a role for dysbiosis in the cardiovascular complications of metabolic dysfunction. This minireview summarizes the available data on the sex-based differences in gut microbiota alterations associated with dietary patterns leading to metabolic impairment. A role for a differential impact of adipose tissue inflammation across sexes in mediating the cardiovascular detrimental phenotype following diet-induced dysbiosis is proposed. Better understanding of this pathway will help introduce early approaches to mitigate cardiovascular deterioration in metabolic disease.
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Affiliation(s)
- Haneen S Dwaib
- Department of Pharmacology and Toxicology, Faculty of Medicine (H.S.D., I.A., G.A., A.E.-Y.), Department of Nutrition and Food Sciences, Faculty of Agricultural and Food Sciences (H.S.D.), American University of Beirut, Beirut, Lebanon; Department of Biochemistry and Molecular Genetics, Faculty of Medicine, American University of Beirut, Beirut, Lebanon (I.A.); Department of Basic Medical Sciences, College of Medicine (A.H.E.), Biomedical and Pharmaceutical Research Unit, QU Health (A.H.E.), Qatar University, Doha, Qatar; Department of Pharmacology and Toxicology, Faculty of Pharmacy, Alexandria University, Alexandria, Egypt (A.E.-Y.); and Faculty of Pharmacy, Alalamein International University, Alalamein, Egypt (A.E.-Y.)
| | - Ibrahim AlZaim
- Department of Pharmacology and Toxicology, Faculty of Medicine (H.S.D., I.A., G.A., A.E.-Y.), Department of Nutrition and Food Sciences, Faculty of Agricultural and Food Sciences (H.S.D.), American University of Beirut, Beirut, Lebanon; Department of Biochemistry and Molecular Genetics, Faculty of Medicine, American University of Beirut, Beirut, Lebanon (I.A.); Department of Basic Medical Sciences, College of Medicine (A.H.E.), Biomedical and Pharmaceutical Research Unit, QU Health (A.H.E.), Qatar University, Doha, Qatar; Department of Pharmacology and Toxicology, Faculty of Pharmacy, Alexandria University, Alexandria, Egypt (A.E.-Y.); and Faculty of Pharmacy, Alalamein International University, Alalamein, Egypt (A.E.-Y.)
| | - Ghina Ajouz
- Department of Pharmacology and Toxicology, Faculty of Medicine (H.S.D., I.A., G.A., A.E.-Y.), Department of Nutrition and Food Sciences, Faculty of Agricultural and Food Sciences (H.S.D.), American University of Beirut, Beirut, Lebanon; Department of Biochemistry and Molecular Genetics, Faculty of Medicine, American University of Beirut, Beirut, Lebanon (I.A.); Department of Basic Medical Sciences, College of Medicine (A.H.E.), Biomedical and Pharmaceutical Research Unit, QU Health (A.H.E.), Qatar University, Doha, Qatar; Department of Pharmacology and Toxicology, Faculty of Pharmacy, Alexandria University, Alexandria, Egypt (A.E.-Y.); and Faculty of Pharmacy, Alalamein International University, Alalamein, Egypt (A.E.-Y.)
| | - Ali H Eid
- Department of Pharmacology and Toxicology, Faculty of Medicine (H.S.D., I.A., G.A., A.E.-Y.), Department of Nutrition and Food Sciences, Faculty of Agricultural and Food Sciences (H.S.D.), American University of Beirut, Beirut, Lebanon; Department of Biochemistry and Molecular Genetics, Faculty of Medicine, American University of Beirut, Beirut, Lebanon (I.A.); Department of Basic Medical Sciences, College of Medicine (A.H.E.), Biomedical and Pharmaceutical Research Unit, QU Health (A.H.E.), Qatar University, Doha, Qatar; Department of Pharmacology and Toxicology, Faculty of Pharmacy, Alexandria University, Alexandria, Egypt (A.E.-Y.); and Faculty of Pharmacy, Alalamein International University, Alalamein, Egypt (A.E.-Y.)
| | - Ahmed El-Yazbi
- Department of Pharmacology and Toxicology, Faculty of Medicine (H.S.D., I.A., G.A., A.E.-Y.), Department of Nutrition and Food Sciences, Faculty of Agricultural and Food Sciences (H.S.D.), American University of Beirut, Beirut, Lebanon; Department of Biochemistry and Molecular Genetics, Faculty of Medicine, American University of Beirut, Beirut, Lebanon (I.A.); Department of Basic Medical Sciences, College of Medicine (A.H.E.), Biomedical and Pharmaceutical Research Unit, QU Health (A.H.E.), Qatar University, Doha, Qatar; Department of Pharmacology and Toxicology, Faculty of Pharmacy, Alexandria University, Alexandria, Egypt (A.E.-Y.); and Faculty of Pharmacy, Alalamein International University, Alalamein, Egypt (A.E.-Y.)
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15
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Hautakangas H, Winsvold BS, Ruotsalainen SE, Bjornsdottir G, Harder AVE, Kogelman LJA, Thomas LF, Noordam R, Benner C, Gormley P, Artto V, Banasik K, Bjornsdottir A, Boomsma DI, Brumpton BM, Burgdorf KS, Buring JE, Chalmer MA, de Boer I, Dichgans M, Erikstrup C, Färkkilä M, Garbrielsen ME, Ghanbari M, Hagen K, Häppölä P, Hottenga JJ, Hrafnsdottir MG, Hveem K, Johnsen MB, Kähönen M, Kristoffersen ES, Kurth T, Lehtimäki T, Lighart L, Magnusson SH, Malik R, Pedersen OB, Pelzer N, Penninx BWJH, Ran C, Ridker PM, Rosendaal FR, Sigurdardottir GR, Skogholt AH, Sveinsson OA, Thorgeirsson TE, Ullum H, Vijfhuizen LS, Widén E, van Dijk KW, Aromaa A, Belin AC, Freilinger T, Ikram MA, Järvelin MR, Raitakari OT, Terwindt GM, Kallela M, Wessman M, Olesen J, Chasman DI, Nyholt DR, Stefánsson H, Stefansson K, van den Maagdenberg AMJM, Hansen TF, Ripatti S, Zwart JA, Palotie A, Pirinen M. Genome-wide analysis of 102,084 migraine cases identifies 123 risk loci and subtype-specific risk alleles. Nat Genet 2022; 54:152-160. [PMID: 35115687 PMCID: PMC8837554 DOI: 10.1038/s41588-021-00990-0] [Citation(s) in RCA: 207] [Impact Index Per Article: 69.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/12/2021] [Accepted: 11/22/2021] [Indexed: 12/11/2022]
Abstract
Migraine affects over a billion individuals worldwide but its genetic underpinning remains largely unknown. Here, we performed a genome-wide association study of 102,084 migraine cases and 771,257 controls and identified 123 loci, of which 86 are previously unknown. These loci provide an opportunity to evaluate shared and distinct genetic components in the two main migraine subtypes: migraine with aura and migraine without aura. Stratification of the risk loci using 29,679 cases with subtype information indicated three risk variants that seem specific for migraine with aura (in HMOX2, CACNA1A and MPPED2), two that seem specific for migraine without aura (near SPINK2 and near FECH) and nine that increase susceptibility for migraine regardless of subtype. The new risk loci include genes encoding recent migraine-specific drug targets, namely calcitonin gene-related peptide (CALCA/CALCB) and serotonin 1F receptor (HTR1F). Overall, genomic annotations among migraine-associated variants were enriched in both vascular and central nervous system tissue/cell types, supporting unequivocally that neurovascular mechanisms underlie migraine pathophysiology.
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Affiliation(s)
- Heidi Hautakangas
- Institute for Molecular Medicine Finland (FIMM), Helsinki Institute of Life Science (HiLIFE), University of Helsinki, Helsinki, Finland
| | - Bendik S Winsvold
- Department of Research, Innovation and Education, Division of Clinical Neuroscience, Oslo University Hospital, Oslo, Norway
- K. G. Jebsen Center for Genetic Epidemiology, Department of Public Health and Nursing, Faculty of Medicine and Health Sciences, Norwegian University of Science and Technology, Trondheim, Norway
- Department of Neurology, Oslo University Hospital, Oslo, Norway
| | - Sanni E Ruotsalainen
- Institute for Molecular Medicine Finland (FIMM), Helsinki Institute of Life Science (HiLIFE), University of Helsinki, Helsinki, Finland
| | | | - Aster V E Harder
- Department of Neurology, Leiden University Medical Center, Leiden, the Netherlands
- Department of Human Genetics, Leiden University Medical Center, Leiden, the Netherlands
| | - Lisette J A Kogelman
- Danish Headache Center, Department of Neurology, Copenhagen University Hospital, Copenhagen, Denmark
| | - Laurent F Thomas
- K. G. Jebsen Center for Genetic Epidemiology, Department of Public Health and Nursing, Faculty of Medicine and Health Sciences, Norwegian University of Science and Technology, Trondheim, Norway
- Department of Clinical and Molecular Medicine, Norwegian University of Science and Technology, Trondheim, Norway
- BioCore - Bioinformatics Core Facility, Norwegian University of Science and Technology, Trondheim, Norway
- Clinic of Laboratory Medicine, St. Olavs Hospital, Trondheim University Hospital, Trondheim, Norway
| | - Raymond Noordam
- Department of Internal Medicine, Section of Gerontology and Geriatrics, Leiden University Medical Center, Leiden, the Netherlands
| | - Christian Benner
- Institute for Molecular Medicine Finland (FIMM), Helsinki Institute of Life Science (HiLIFE), University of Helsinki, Helsinki, Finland
| | | | - Ville Artto
- Department of Neurology, Helsinki University Central Hospital, Helsinki, Finland
| | - Karina Banasik
- Novo Nordic Foundation Center for Protein Research, Copenhagen University, Copenhagen, Denmark
| | | | - Dorret I Boomsma
- Netherlands Twin Register, Department of Biological Psychology, Vrije Universiteit, Amsterdam, the Netherlands
| | - Ben M Brumpton
- K. G. Jebsen Center for Genetic Epidemiology, Department of Public Health and Nursing, Faculty of Medicine and Health Sciences, Norwegian University of Science and Technology, Trondheim, Norway
| | | | - Julie E Buring
- Division of Preventive Medicine, Brigham and Women's Hospital, Boston, MA, USA
- Harvard Medical School, Boston, MA, USA
| | - Mona Ameri Chalmer
- Danish Headache Center, Department of Neurology, Copenhagen University Hospital, Copenhagen, Denmark
| | - Irene de Boer
- Department of Neurology, Leiden University Medical Center, Leiden, the Netherlands
| | - Martin Dichgans
- Institute for Stroke and Dementia Research, University Hospital, LMU Munich, Munich, Germany
- Munich Cluster for Systems Neurology (Synergy), Munich, Germany
| | - Christian Erikstrup
- Department of Clinical Immunology, Aarhus University Hospital, Aarhus, Denmark
| | - Markus Färkkilä
- Department of Neurology, Helsinki University Central Hospital, Helsinki, Finland
| | - Maiken Elvestad Garbrielsen
- K. G. Jebsen Center for Genetic Epidemiology, Department of Public Health and Nursing, Faculty of Medicine and Health Sciences, Norwegian University of Science and Technology, Trondheim, Norway
| | - Mohsen Ghanbari
- Department of Epidemiology, Erasmus University Medical Center, Rotterdam, the Netherlands
| | - Knut Hagen
- Department of Neuromedicine and Movement Science, Faculty of Medicine and Health Sciences, Norwegian University of Science and Technology (NTNU), Trondheim, Norway
- Clinical Research Unit Central Norway, St. Olavs University Hospital, Trondheim, Norway
| | - Paavo Häppölä
- Institute for Molecular Medicine Finland (FIMM), Helsinki Institute of Life Science (HiLIFE), University of Helsinki, Helsinki, Finland
| | - Jouke-Jan Hottenga
- Netherlands Twin Register, Department of Biological Psychology, Vrije Universiteit, Amsterdam, the Netherlands
| | | | - Kristian Hveem
- K. G. Jebsen Center for Genetic Epidemiology, Department of Public Health and Nursing, Faculty of Medicine and Health Sciences, Norwegian University of Science and Technology, Trondheim, Norway
- HUNT Research Center, Department of Public Health and Nursing, Faculty of Medicine and Health Sciences, Norwegian University of Science and Technology, Trondheim, Norway
| | - Marianne Bakke Johnsen
- K. G. Jebsen Center for Genetic Epidemiology, Department of Public Health and Nursing, Faculty of Medicine and Health Sciences, Norwegian University of Science and Technology, Trondheim, Norway
- Institute of Clinical Medicine, Faculty of Medicine, University of Oslo, Oslo, Norway
- Research and Communication Unit for Musculoskeletal Health (FORMI), Department of Research, Innovation and Education, Division of Clinical Neuroscience, Oslo University Hospital, Oslo, Norway
| | - Mika Kähönen
- Department of Clinical Physiology, Tampere University Hospital, and Finnish Cardiovascular Research Center - Tampere, Faculty of Medicine and Health Technology, Tampere University, Tampere, Finland
| | - Espen S Kristoffersen
- Research and Communication Unit for Musculoskeletal Health (FORMI), Department of Research, Innovation and Education, Division of Clinical Neuroscience, Oslo University Hospital, Oslo, Norway
- Department of General Practice, Institute of Health and Society, University of Oslo, Oslo, Norway
- Department of Neurology, Akershus University Hospital, Lørenskog, Norway
| | - Tobias Kurth
- Institute of Public Health, Charité - Universitätsmedizin Berlin, Berlin, Germany
| | - Terho Lehtimäki
- Department of Clinical Chemistry, Fimlab Laboratories, and Finnish Cardiovascular Research Center - Tampere, Faculty of Medicine and Health Technology, Tampere University, Tampere, Finland
| | - Lannie Lighart
- Netherlands Twin Register, Department of Biological Psychology, Vrije Universiteit, Amsterdam, the Netherlands
| | | | - Rainer Malik
- Institute for Stroke and Dementia Research, University Hospital, LMU Munich, Munich, Germany
| | - Ole Birger Pedersen
- Department of Clinical Immunology, Zealand University Hospital, Køge, Denmark
| | - Nadine Pelzer
- Department of Neurology, Leiden University Medical Center, Leiden, the Netherlands
| | - Brenda W J H Penninx
- Department of Psychiatry, Amsterdam UMC, Vrije Universiteit, Amsterdam Public Health Research Institute, Amsterdam, the Netherlands
- GGZ inGeest Specialized Mental Health Care, Amsterdam, the Netherlands
| | - Caroline Ran
- Department of Neuroscience, Karolinska Institutet, Stockholm, Sweden
| | - Paul M Ridker
- Division of Preventive Medicine, Brigham and Women's Hospital, Boston, MA, USA
- Harvard Medical School, Boston, MA, USA
| | - Frits R Rosendaal
- Department of Clinical Epidemiology, Leiden University Medical Center, Leiden, the Netherlands
| | | | - Anne Heidi Skogholt
- K. G. Jebsen Center for Genetic Epidemiology, Department of Public Health and Nursing, Faculty of Medicine and Health Sciences, Norwegian University of Science and Technology, Trondheim, Norway
| | | | | | - Henrik Ullum
- Department of Clinical Immunology, Copenhagen University Hospital, Rigshospitalet, Copenhagen, Denmark
| | - Lisanne S Vijfhuizen
- Department of Human Genetics, Leiden University Medical Center, Leiden, the Netherlands
| | - Elisabeth Widén
- Institute for Molecular Medicine Finland (FIMM), Helsinki Institute of Life Science (HiLIFE), University of Helsinki, Helsinki, Finland
| | - Ko Willems van Dijk
- Department of Human Genetics, Leiden University Medical Center, Leiden, the Netherlands
- Department of Internal Medicine, Division of Endocrinology, Leiden University Medical Center, Leiden, the Netherlands
| | - Arpo Aromaa
- National Public Health Institute (Finnish Institute for Health and Welfare - THL), Helsinki, Finland
| | | | - Tobias Freilinger
- Klinikum Passau, Department of Neurology, Passau, Germany
- Centre of Neurology, Hertie Institute for Clinical Brain Research, Tuebingen, Germany
| | - M Arfan Ikram
- Department of Epidemiology, Erasmus University Medical Center, Rotterdam, the Netherlands
| | - Marjo-Riitta Järvelin
- Department of Epidemiology and Biostatistics, MRC-PHE Centre for Environment and Health, School of Public Health, Imperial College London, London, UK
- Center for Life Course Health Research, Faculty of Medicine, University of Oulu, Oulu, Finland
- Unit of Primary Health Care, Oulu University Hospital, Oulu, Finland
- Department of Life Sciences, College of Health and Life Sciences, Brunel University London, London, UK
| | - Olli T Raitakari
- Centre for Population Health Research, University of Turku and Turku University Hospital, Turku, Finland
- Research Centre of Applied and Preventive Cardiovascular Medicine, University of Turku, Turku, Finland
- Department of Clinical Physiology and Nuclear Medicine, Turku University Hospital, Turku, Finland
| | - Gisela M Terwindt
- Department of Neurology, Leiden University Medical Center, Leiden, the Netherlands
| | - Mikko Kallela
- Department of Neurology, Helsinki University Central Hospital, Helsinki, Finland
| | - Maija Wessman
- Institute for Molecular Medicine Finland (FIMM), Helsinki Institute of Life Science (HiLIFE), University of Helsinki, Helsinki, Finland
- Folkhälsan Research Center, Helsinki, Finland
| | - Jes Olesen
- Danish Headache Center, Department of Neurology, Copenhagen University Hospital, Copenhagen, Denmark
| | - Daniel I Chasman
- Division of Preventive Medicine, Brigham and Women's Hospital, Boston, MA, USA
- Harvard Medical School, Boston, MA, USA
| | - Dale R Nyholt
- School of Biomedical Sciences and Centre for Genomics and Personalised Health, Faculty of Health, Queensland University of Technology, Brisbane, QLD, Australia
| | | | | | - Arn M J M van den Maagdenberg
- Department of Neurology, Leiden University Medical Center, Leiden, the Netherlands
- Department of Human Genetics, Leiden University Medical Center, Leiden, the Netherlands
| | - Thomas Folkmann Hansen
- Danish Headache Center, Department of Neurology, Copenhagen University Hospital, Copenhagen, Denmark
- Novo Nordic Foundation Center for Protein Research, Copenhagen University, Copenhagen, Denmark
| | - Samuli Ripatti
- Institute for Molecular Medicine Finland (FIMM), Helsinki Institute of Life Science (HiLIFE), University of Helsinki, Helsinki, Finland
- Broad Institute of MIT and Harvard, Cambridge, MA, USA
- Department of Public Health, University of Helsinki, Helsinki, Finland
| | - John-Anker Zwart
- Department of Research, Innovation and Education, Division of Clinical Neuroscience, Oslo University Hospital, Oslo, Norway
- K. G. Jebsen Center for Genetic Epidemiology, Department of Public Health and Nursing, Faculty of Medicine and Health Sciences, Norwegian University of Science and Technology, Trondheim, Norway
- Institute of Clinical Medicine, Faculty of Medicine, University of Oslo, Oslo, Norway
| | - Aarno Palotie
- Institute for Molecular Medicine Finland (FIMM), Helsinki Institute of Life Science (HiLIFE), University of Helsinki, Helsinki, Finland
- Analytic and Translational Genetics Unit, Department of Medicine, Department of Neurology and Department of Psychiatry, Massachusetts General Hospital, Boston, MA, USA
- The Stanley Center for Psychiatric Research and Program in Medical and Population Genetics, The Broad Institute of MIT and Harvard, Cambridge, MA, USA
| | - Matti Pirinen
- Institute for Molecular Medicine Finland (FIMM), Helsinki Institute of Life Science (HiLIFE), University of Helsinki, Helsinki, Finland.
- Department of Public Health, University of Helsinki, Helsinki, Finland.
- Department of Mathematics and Statistics, University of Helsinki, Helsinki, Finland.
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16
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Sauteraud R, Stahl JM, James J, Englebright M, Chen F, Zhan X, Carrel L, Liu DJ. Inferring genes that escape X-Chromosome inactivation reveals important contribution of variable escape genes to sex-biased diseases. Genome Res 2021; 31:1629-1637. [PMID: 34426515 PMCID: PMC8415373 DOI: 10.1101/gr.275677.121] [Citation(s) in RCA: 32] [Impact Index Per Article: 8.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/29/2021] [Accepted: 07/23/2021] [Indexed: 01/07/2023]
Abstract
The X Chromosome plays an important role in human development and disease. However, functional genomic and disease association studies of X genes greatly lag behind autosomal gene studies, in part owing to the unique biology of X-Chromosome inactivation (XCI). Because of XCI, most genes are only expressed from one allele. Yet, ∼30% of X genes “escape” XCI and are transcribed from both alleles, many only in a proportion of the population. Such interindividual differences are likely to be disease relevant, particularly for sex-biased disorders. To understand the functional biology for X-linked genes, we developed X-Chromosome inactivation for RNA-seq (XCIR), a novel approach to identify escape genes using bulk RNA-seq data. Our method, available as an R package, is more powerful than alternative approaches and is computationally efficient to handle large population-scale data sets. Using annotated XCI states, we examined the contribution of X-linked genes to the disease heritability in the United Kingdom Biobank data set. We show that escape and variable escape genes explain the largest proportion of X heritability, which is in large part attributable to X genes with Y homology. Finally, we investigated the role of each XCI state in sex-biased diseases and found that although XY homologous gene pairs have a larger overall effect size, enrichment for variable escape genes is significantly increased in female-biased diseases. Our results, for the first time, quantitate the importance of variable escape genes for the etiology of sex-biased disease, and our pipeline allows analysis of larger data sets for a broad range of phenotypes.
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Affiliation(s)
- Renan Sauteraud
- Department of Public Health Sciences, Penn State College of Medicine, Hershey, Pennsylvania 17033, USA
| | - Jill M Stahl
- Department of Biochemistry and Molecular Biology, Penn State College of Medicine, Hershey, Pennsylvania 17033, USA
| | - Jesica James
- Department of Biochemistry and Molecular Biology, Penn State College of Medicine, Hershey, Pennsylvania 17033, USA
| | - Marisa Englebright
- Department of Biochemistry and Molecular Biology, Penn State College of Medicine, Hershey, Pennsylvania 17033, USA
| | - Fang Chen
- Department of Public Health Sciences, Penn State College of Medicine, Hershey, Pennsylvania 17033, USA.,Institute for Personalized Medicine, Penn State College of Medicine, Hershey, Pennsylvania 17033, USA
| | - Xiaowei Zhan
- Department of Clinical Science, Quantitative Biomedical Research Center, University of Texas Southwestern Medical Center, Dallas, Texas 75390-8821, USA
| | - Laura Carrel
- Department of Biochemistry and Molecular Biology, Penn State College of Medicine, Hershey, Pennsylvania 17033, USA
| | - Dajiang J Liu
- Department of Public Health Sciences, Penn State College of Medicine, Hershey, Pennsylvania 17033, USA.,Department of Biochemistry and Molecular Biology, Penn State College of Medicine, Hershey, Pennsylvania 17033, USA.,Institute for Personalized Medicine, Penn State College of Medicine, Hershey, Pennsylvania 17033, USA
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17
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The genetic architecture of primary biliary cholangitis. Eur J Med Genet 2021; 64:104292. [PMID: 34303876 DOI: 10.1016/j.ejmg.2021.104292] [Citation(s) in RCA: 20] [Impact Index Per Article: 5.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/10/2021] [Revised: 07/03/2021] [Accepted: 07/21/2021] [Indexed: 12/12/2022]
Abstract
Primary biliary cholangitis (PBC) is a rare autoimmune disease of the liver affecting the small bile ducts. From a genetic point of view, PBC is a complex trait and several genetic and environmental factors have been called in action to explain its etiopathogenesis. Similarly to other complex traits, PBC has benefited from the introduction of genome-wide association studies (GWAS), which identified many variants predisposing or protecting toward the development of the disease. While a progressive endeavour toward the characterization of candidate loci and downstream pathways is currently ongoing, there is still a relatively large portion of heritability of PBC to be revealed. In addition, genetic variation behind progression of the disease and therapeutic response are mostly to be investigated yet. This review outlines the state-of-the-art regarding the genetic architecture of PBC and provides some hints for future investigations, focusing on the study of gene-gene interactions, the application of whole-genome sequencing techniques, and the investigation of X chromosome that can be helpful to cover the missing heritability gap in PBC.
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18
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Hägg S, Jylhävä J. Sex differences in biological aging with a focus on human studies. eLife 2021; 10:e63425. [PMID: 33982659 PMCID: PMC8118651 DOI: 10.7554/elife.63425] [Citation(s) in RCA: 209] [Impact Index Per Article: 52.3] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/29/2020] [Accepted: 05/04/2021] [Indexed: 02/06/2023] Open
Abstract
Aging is a complex biological process characterized by hallmark features accumulating over the life course, shaping the individual's aging trajectory and subsequent disease risks. There is substantial individual variability in the aging process between men and women. In general, women live longer than men, consistent with lower biological ages as assessed by molecular biomarkers, but there is a paradox. Women are frailer and have worse health at the end of life, while men still perform better in physical function examinations. Moreover, many age-related diseases show sex-specific patterns. In this review, we aim to summarize the current knowledge on sexual dimorphism in human studies, with support from animal research, on biological aging and illnesses. We also attempt to place it in the context of the theories of aging, as well as discuss the explanations for the sex differences, for example, the sex-chromosome linked mechanisms and hormonally driven differences.
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Affiliation(s)
- Sara Hägg
- Department of Medical Epidemiology and Biostatistics, Karolinska InstitutetStockholmSweden
| | - Juulia Jylhävä
- Department of Medical Epidemiology and Biostatistics, Karolinska InstitutetStockholmSweden
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19
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Wang D, Tang L, Wu Y, Fan C, Zhang S, Xiang B, Zhou M, Li X, Li Y, Li G, Xiong W, Zeng Z, Guo C. Abnormal X chromosome inactivation and tumor development. Cell Mol Life Sci 2020; 77:2949-2958. [PMID: 32040694 PMCID: PMC11104905 DOI: 10.1007/s00018-020-03469-z] [Citation(s) in RCA: 21] [Impact Index Per Article: 4.2] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/26/2019] [Revised: 01/17/2020] [Accepted: 01/21/2020] [Indexed: 12/13/2022]
Abstract
During embryonic development, one of the two X chromosomes of a mammalian female cell is randomly inactivated by the X chromosome inactivation mechanism, which is mainly dependent on the regulation of the non-coding RNA X-inactive specific transcript at the X chromosome inactivation center. There are three proteins that are essential for X-inactive specific transcript to function properly: scaffold attachment factor-A, lamin B receptor, and SMRT- and HDAC-associated repressor protein. In addition, the absence of X-inactive specific transcript expression promotes tumor development. During the process of chromosome inactivation, some tumor suppressor genes escape inactivation of the X chromosome and thereby continue to play a role in tumor suppression. A well-functioning tumor suppressor gene on the idle X chromosome in women is one of the reasons they have a lower propensity to develop cancer than men, women thereby benefit from this enhanced tumor suppression. This review will explore the mechanism of X chromosome inactivation, discuss the relationship between X chromosome inactivation and tumorigenesis, and consider the consequent sex differences in cancer.
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Affiliation(s)
- Dan Wang
- Department of Stomatology, NHC Key Laboratory of Carcinogenesis, Xiangya Hospital, Central South University, Changsha, Hunan, China
- Key Laboratory of Carcinogenesis and Cancer Invasion of the Chinese Ministry of Education, Cancer Research Institute, Central South University, Changsha, Hunan, China
- Hunan Key Laboratory of Nonresolving Inflammation and Cancer, Disease Genome Research Center, The Third Xiangya Hospital, Central South University, Changsha, Hunan, China
| | - Le Tang
- Key Laboratory of Carcinogenesis and Cancer Invasion of the Chinese Ministry of Education, Cancer Research Institute, Central South University, Changsha, Hunan, China
| | - Yingfen Wu
- Key Laboratory of Carcinogenesis and Cancer Invasion of the Chinese Ministry of Education, Cancer Research Institute, Central South University, Changsha, Hunan, China
| | - Chunmei Fan
- Key Laboratory of Carcinogenesis and Cancer Invasion of the Chinese Ministry of Education, Cancer Research Institute, Central South University, Changsha, Hunan, China
| | - Shanshan Zhang
- Department of Stomatology, NHC Key Laboratory of Carcinogenesis, Xiangya Hospital, Central South University, Changsha, Hunan, China
| | - Bo Xiang
- Key Laboratory of Carcinogenesis and Cancer Invasion of the Chinese Ministry of Education, Cancer Research Institute, Central South University, Changsha, Hunan, China
| | - Ming Zhou
- Key Laboratory of Carcinogenesis and Cancer Invasion of the Chinese Ministry of Education, Cancer Research Institute, Central South University, Changsha, Hunan, China
| | - Xiaoling Li
- Key Laboratory of Carcinogenesis and Cancer Invasion of the Chinese Ministry of Education, Cancer Research Institute, Central South University, Changsha, Hunan, China
| | - Yong Li
- Department of Medicine, Dan L Duncan Comprehensive Cancer Center, Baylor College of Medicine, Houston, Texas, USA
| | - Guiyuan Li
- Key Laboratory of Carcinogenesis and Cancer Invasion of the Chinese Ministry of Education, Cancer Research Institute, Central South University, Changsha, Hunan, China
| | - Wei Xiong
- Key Laboratory of Carcinogenesis and Cancer Invasion of the Chinese Ministry of Education, Cancer Research Institute, Central South University, Changsha, Hunan, China
| | - Zhaoyang Zeng
- Key Laboratory of Carcinogenesis and Cancer Invasion of the Chinese Ministry of Education, Cancer Research Institute, Central South University, Changsha, Hunan, China
| | - Can Guo
- Department of Stomatology, NHC Key Laboratory of Carcinogenesis, Xiangya Hospital, Central South University, Changsha, Hunan, China.
- Key Laboratory of Carcinogenesis and Cancer Invasion of the Chinese Ministry of Education, Cancer Research Institute, Central South University, Changsha, Hunan, China.
- Hunan Key Laboratory of Nonresolving Inflammation and Cancer, Disease Genome Research Center, The Third Xiangya Hospital, Central South University, Changsha, Hunan, China.
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20
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Tam V, Patel N, Turcotte M, Bossé Y, Paré G, Meyre D. Benefits and limitations of genome-wide association studies. Nat Rev Genet 2019; 20:467-484. [PMID: 31068683 DOI: 10.1038/s41576-019-0127-1] [Citation(s) in RCA: 1113] [Impact Index Per Article: 185.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/21/2022]
Abstract
Genome-wide association studies (GWAS) involve testing genetic variants across the genomes of many individuals to identify genotype-phenotype associations. GWAS have revolutionized the field of complex disease genetics over the past decade, providing numerous compelling associations for human complex traits and diseases. Despite clear successes in identifying novel disease susceptibility genes and biological pathways and in translating these findings into clinical care, GWAS have not been without controversy. Prominent criticisms include concerns that GWAS will eventually implicate the entire genome in disease predisposition and that most association signals reflect variants and genes with no direct biological relevance to disease. In this Review, we comprehensively assess the benefits and limitations of GWAS in human populations and discuss the relevance of performing more GWAS.
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Affiliation(s)
- Vivian Tam
- Department of Health Research Methods, Evidence, and Impact, McMaster University, Hamilton, Ontario, Canada
| | - Nikunj Patel
- Department of Health Research Methods, Evidence, and Impact, McMaster University, Hamilton, Ontario, Canada
| | - Michelle Turcotte
- Department of Health Research Methods, Evidence, and Impact, McMaster University, Hamilton, Ontario, Canada
| | - Yohan Bossé
- Institut Universitaire de Cardiologie et de Pneumologie de Québec-Université Laval, Québec City, Québec, Canada.,Department of Molecular Medicine, Laval University, Québec City, Quebec, Canada
| | - Guillaume Paré
- Department of Health Research Methods, Evidence, and Impact, McMaster University, Hamilton, Ontario, Canada.,Department of Pathology and Molecular Medicine, McMaster University, Hamilton, Ontario, Canada
| | - David Meyre
- Department of Health Research Methods, Evidence, and Impact, McMaster University, Hamilton, Ontario, Canada. .,Department of Pathology and Molecular Medicine, McMaster University, Hamilton, Ontario, Canada. .,Inserm UMRS 954 N-GERE (Nutrition-Genetics-Environmental Risks), University of Lorraine, Faculty of Medicine, Nancy, France.
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21
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Matarrese P, Tieri P, Anticoli S, Ascione B, Conte M, Franceschi C, Malorni W, Salvioli S, Ruggieri A. X-chromosome-linked miR548am-5p is a key regulator of sex disparity in the susceptibility to mitochondria-mediated apoptosis. Cell Death Dis 2019; 10:673. [PMID: 31511496 PMCID: PMC6739406 DOI: 10.1038/s41419-019-1888-3] [Citation(s) in RCA: 19] [Impact Index Per Article: 3.2] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/15/2019] [Revised: 06/24/2019] [Accepted: 07/18/2019] [Indexed: 12/19/2022]
Abstract
Sex dimorphism in cell response to stress has previously been investigated by different research groups. This dimorphism could be at least in part accounted for by sex-biased expression of regulatory elements such as microRNAs (miRs). In order to spot previously unknown miR expression differences we took advantage of prior knowledge on specialized databases to identify X chromosome-encoded miRs potentially escaping X chromosome inactivation (XCI). MiR-548am-5p emerged as potentially XCI escaper and was experimentally verified to be significantly up-regulated in human XX primary dermal fibroblasts (DFs) compared to XY ones. Accordingly, miR-548am-5p target mRNAs, e.g. the transcript for Bax, was differently modulated in XX and XY DFs. Functional analyses indicated that XY DFs were more prone to mitochondria-mediated apoptosis than XX ones. Experimentally induced overexpression of miR548am-5p in XY cells by lentivirus vector transduction decreased apoptosis susceptibility, whereas its down-regulation in XX cells enhanced apoptosis susceptibility. These data indicate that this approach could be used to identify previously unreported sex-biased differences in miR expression and that a miR identified with this approach, miR548am-5p, can account for sex-dependent differences observed in the susceptibility to mitochondrial apoptosis of human DFs.
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Affiliation(s)
- Paola Matarrese
- Center for Gender Specific Medicine, Istituto Superiore di Sanità, viale Regina Elena 299, Rome, Italy
| | - Paolo Tieri
- CNR National Research Council, IAC Institute for Applied Computing, Via dei Taurini 19, Rome, Italy.,Data Science Program, La Sapienza University of Rome, Rome, Italy
| | - Simona Anticoli
- Center for Gender Specific Medicine, Istituto Superiore di Sanità, viale Regina Elena 299, Rome, Italy
| | - Barbara Ascione
- Center for Gender Specific Medicine, Istituto Superiore di Sanità, viale Regina Elena 299, Rome, Italy
| | - Maria Conte
- Department of Experimental, Diagnostic and Specialty Medicine (DIMES), University of Bologna, Bologna, Italy.,Interdepartmental Centre "L. Galvani" (CIG), University of Bologna, via San Giacomo 12, 40126, Bologna, Italy
| | - Claudio Franceschi
- IRCCS Istituto delle Scienze Neurologiche di Bologna, Via Altura 3, 40139, Bologna, Italy.,Lobachevsky State University of Nizhny Novgorod, Nizhny Novgorod, Russia
| | - Walter Malorni
- Center for Gender Specific Medicine, Istituto Superiore di Sanità, viale Regina Elena 299, Rome, Italy.,School of Mathematical, Physical and Natural Sciences and Faculty of Medicine, University of Tor Vergata, Rome, Italy
| | - Stefano Salvioli
- Department of Experimental, Diagnostic and Specialty Medicine (DIMES), University of Bologna, Bologna, Italy. .,Interdepartmental Centre "L. Galvani" (CIG), University of Bologna, via San Giacomo 12, 40126, Bologna, Italy.
| | - Anna Ruggieri
- Center for Gender Specific Medicine, Istituto Superiore di Sanità, viale Regina Elena 299, Rome, Italy.
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22
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Naqvi S, Godfrey AK, Hughes JF, Goodheart ML, Mitchell RN, Page DC. Conservation, acquisition, and functional impact of sex-biased gene expression in mammals. Science 2019; 365:eaaw7317. [PMID: 31320509 PMCID: PMC6896219 DOI: 10.1126/science.aaw7317] [Citation(s) in RCA: 148] [Impact Index Per Article: 24.7] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/19/2019] [Accepted: 06/12/2019] [Indexed: 12/15/2022]
Abstract
Sex differences abound in human health and disease, as they do in other mammals used as models. The extent to which sex differences are conserved at the molecular level across species and tissues is unknown. We surveyed sex differences in gene expression in human, macaque, mouse, rat, and dog, across 12 tissues. In each tissue, we identified hundreds of genes with conserved sex-biased expression-findings that, combined with genomic analyses of human height, explain ~12% of the difference in height between females and males. We surmise that conserved sex biases in expression of genes otherwise operating equivalently in females and males contribute to sex differences in traits. However, most sex-biased expression arose during the mammalian radiation, which suggests that careful attention to interspecies divergence is needed when modeling human sex differences.
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Affiliation(s)
- Sahin Naqvi
- Whitehead Institute, Cambridge, MA 02142, USA
- Department of Biology, Massachusetts Institute of Technology, Cambridge, MA 02139, USA
| | - Alexander K Godfrey
- Whitehead Institute, Cambridge, MA 02142, USA
- Department of Biology, Massachusetts Institute of Technology, Cambridge, MA 02139, USA
| | | | - Mary L Goodheart
- Whitehead Institute, Cambridge, MA 02142, USA
- Howard Hughes Medical Institute, Whitehead Institute, Cambridge, MA 02142, USA
| | - Richard N Mitchell
- Brigham and Women's Hospital, Harvard Medical School, Boston, MA 02115, USA
| | - David C Page
- Whitehead Institute, Cambridge, MA 02142, USA.
- Department of Biology, Massachusetts Institute of Technology, Cambridge, MA 02139, USA
- Howard Hughes Medical Institute, Whitehead Institute, Cambridge, MA 02142, USA
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23
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Khramtsova EA, Davis LK, Stranger BE. The role of sex in the genomics of human complex traits. Nat Rev Genet 2019; 20:173-190. [PMID: 30581192 DOI: 10.1038/s41576-018-0083-1] [Citation(s) in RCA: 197] [Impact Index Per Article: 32.8] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 02/07/2023]
Abstract
Nearly all human complex traits and disease phenotypes exhibit some degree of sex differences, including differences in prevalence, age of onset, severity or disease progression. Until recently, the underlying genetic mechanisms of such sex differences have been largely unexplored. Advances in genomic technologies and analytical approaches are now enabling a deeper investigation into the effect of sex on human health traits. In this Review, we discuss recent insights into the genetic models and mechanisms that lead to sex differences in complex traits. This knowledge is critical for developing deeper insight into the fundamental biology of sex differences and disease processes, thus facilitating precision medicine.
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Affiliation(s)
- Ekaterina A Khramtsova
- Section of Genetic Medicine, Department of Medicine, University of Chicago, Chicago, IL, USA.,Institute for Genomics and Systems Biology, University of Chicago, Chicago, IL, USA
| | - Lea K Davis
- Division of Medical Genetics, Department of Medicine, Vanderbilt University Medical Center, Nashville, TN, USA. .,Department of Psychiatry and Behavioral Sciences, Vanderbilt University Medical Center, Nashville, TN, USA.
| | - Barbara E Stranger
- Section of Genetic Medicine, Department of Medicine, University of Chicago, Chicago, IL, USA. .,Institute for Genomics and Systems Biology, University of Chicago, Chicago, IL, USA. .,Center for Data Intensive Science, University of Chicago, Chicago, IL, USA.
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24
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Sidorenko J, Kassam I, Kemper KE, Zeng J, Lloyd-Jones LR, Montgomery GW, Gibson G, Metspalu A, Esko T, Yang J, McRae AF, Visscher PM. The effect of X-linked dosage compensation on complex trait variation. Nat Commun 2019; 10:3009. [PMID: 31285442 PMCID: PMC6614401 DOI: 10.1038/s41467-019-10598-y] [Citation(s) in RCA: 40] [Impact Index Per Article: 6.7] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/03/2018] [Accepted: 05/09/2019] [Indexed: 12/21/2022] Open
Abstract
Quantitative genetics theory predicts that X-chromosome dosage compensation (DC) will have a detectable effect on the amount of genetic and therefore phenotypic trait variances at associated loci in males and females. Here, we systematically examine the role of DC in humans in 20 complex traits in a sample of more than 450,000 individuals from the UK Biobank and 1600 gene expression traits from a sample of 2000 individuals as well as across-tissue gene expression from the GTEx resource. We find approximately twice as much X-linked genetic variation across the UK Biobank traits in males (mean h2SNP = 0.63%) compared to females (mean h2SNP = 0.30%), confirming the predicted DC effect. Our DC estimates for complex traits and gene expression are consistent with a small proportion of genes escaping X-inactivation in a trait- and tissue-dependent manner. Finally, we highlight examples of biologically relevant X-linked heterogeneity between the sexes that bias DC estimates if unaccounted for. Dosage compensation (DC) on the X chromosome has predictable effects on genetic and phenotypic trait variance. Here, the authors use information for 20 quantitative traits in the UK Biobank and across-tissue gene expression to compare X-linked heritability and the effects of trait-associated SNPs between the sexes.
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Affiliation(s)
- Julia Sidorenko
- Institute for Molecular Bioscience, The University of Queensland, Brisbane, 4072, QLD, Australia.
| | - Irfahan Kassam
- Institute for Molecular Bioscience, The University of Queensland, Brisbane, 4072, QLD, Australia
| | - Kathryn E Kemper
- Institute for Molecular Bioscience, The University of Queensland, Brisbane, 4072, QLD, Australia
| | - Jian Zeng
- Institute for Molecular Bioscience, The University of Queensland, Brisbane, 4072, QLD, Australia
| | - Luke R Lloyd-Jones
- Institute for Molecular Bioscience, The University of Queensland, Brisbane, 4072, QLD, Australia
| | - Grant W Montgomery
- Institute for Molecular Bioscience, The University of Queensland, Brisbane, 4072, QLD, Australia
| | - Greg Gibson
- School of Biology and Centre for Integrative Genomics, Georgia Institute of Technology, Atlanta, GA, 30332, USA
| | - Andres Metspalu
- Estonian Genome Centre, Institute of Genomics, University of Tartu, Tartu, 51010, Estonia
| | - Tonu Esko
- Estonian Genome Centre, Institute of Genomics, University of Tartu, Tartu, 51010, Estonia
| | - Jian Yang
- Institute for Molecular Bioscience, The University of Queensland, Brisbane, 4072, QLD, Australia.,Queensland Brain Institute, The University of Queensland, Brisbane, 4072, QLD, Australia
| | - Allan F McRae
- Institute for Molecular Bioscience, The University of Queensland, Brisbane, 4072, QLD, Australia
| | - Peter M Visscher
- Institute for Molecular Bioscience, The University of Queensland, Brisbane, 4072, QLD, Australia. .,Queensland Brain Institute, The University of Queensland, Brisbane, 4072, QLD, Australia.
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25
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Zuo XY, Feng QS, Sun J, Wei PP, Chin YM, Guo YM, Xia YF, Li B, Xia XJ, Jia WH, Liu JJ, Khoo ASB, Mushiroda T, Ng CC, Su WH, Zeng YX, Bei JX. X-chromosome association study reveals genetic susceptibility loci of nasopharyngeal carcinoma. Biol Sex Differ 2019; 10:13. [PMID: 30909962 PMCID: PMC6434801 DOI: 10.1186/s13293-019-0227-9] [Citation(s) in RCA: 12] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 01/09/2019] [Accepted: 02/27/2019] [Indexed: 02/08/2023] Open
Abstract
Background The male predominance in the incidence of nasopharyngeal carcinoma (NPC) suggests the contribution of the X chromosome to the susceptibility of NPC. However, no X-linked susceptibility loci have been examined by genome-wide association studies (GWASs) for NPC by far. Methods To understand the contribution of the X chromosome in NPC susceptibility, we conducted an X chromosome-wide association analysis on 1615 NPC patients and 1025 healthy controls of Guangdong Chinese, followed by two validation analyses in Taiwan Chinese (n = 562) and Malaysian Chinese (n = 716). Results Firstly, the proportion of variance of X-linked loci over phenotypic variance was estimated in the discovery samples, which revealed that the phenotypic variance explained by X chromosome polymorphisms was estimated to be 12.63% (non-dosage compensation model) in males, as compared with 0.0001% in females. This suggested that the contribution of X chromosome to the genetic variance of NPC should not be neglected. Secondly, association analysis revealed that rs5927056 in DMD gene achieved X chromosome-wide association significance in the discovery sample (OR = 0.81, 95% CI 0.73–0.89, P = 1.49 × 10−5). Combined analysis revealed rs5927056 for DMD gene with suggestive significance (P = 9.44 × 10−5). Moreover, the female-specific association of rs5933886 in ARHGAP6 gene (OR = 0.62, 95%CI: 0.47–0.81, P = 4.37 × 10−4) was successfully replicated in Taiwan Chinese (P = 1.64 × 10−2). rs5933886 also showed nominally significant gender × SNP interaction in both Guangdong (P = 6.25 × 10−4) and Taiwan datasets (P = 2.99 × 10−2). Conclusion Our finding reveals new susceptibility loci at the X chromosome conferring risk of NPC and supports the value of including the X chromosome in large-scale association studies. Electronic supplementary material The online version of this article (10.1186/s13293-019-0227-9) contains supplementary material, which is available to authorized users.
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Affiliation(s)
- Xiao-Yu Zuo
- State Key Laboratory of Oncology in South China, Collaborative Innovation Center for Cancer Medicine, Guangdong Key Laboratory of Nasopharyngeal Carcinoma Diagnosis and Therapy, Sun Yat-sen University Cancer Center, Guangzhou, 510060, People's Republic of China
| | - Qi-Sheng Feng
- State Key Laboratory of Oncology in South China, Collaborative Innovation Center for Cancer Medicine, Guangdong Key Laboratory of Nasopharyngeal Carcinoma Diagnosis and Therapy, Sun Yat-sen University Cancer Center, Guangzhou, 510060, People's Republic of China
| | - Jian Sun
- State Key Laboratory of Oncology in South China, Collaborative Innovation Center for Cancer Medicine, Guangdong Key Laboratory of Nasopharyngeal Carcinoma Diagnosis and Therapy, Sun Yat-sen University Cancer Center, Guangzhou, 510060, People's Republic of China
| | - Pan-Pan Wei
- State Key Laboratory of Oncology in South China, Collaborative Innovation Center for Cancer Medicine, Guangdong Key Laboratory of Nasopharyngeal Carcinoma Diagnosis and Therapy, Sun Yat-sen University Cancer Center, Guangzhou, 510060, People's Republic of China
| | - Yoon-Ming Chin
- Institute of Biological Sciences, Faculty of Science, University of Malaya, 50603, Kuala Lumpur, Malaysia
| | - Yun-Miao Guo
- State Key Laboratory of Oncology in South China, Collaborative Innovation Center for Cancer Medicine, Guangdong Key Laboratory of Nasopharyngeal Carcinoma Diagnosis and Therapy, Sun Yat-sen University Cancer Center, Guangzhou, 510060, People's Republic of China
| | - Yun-Fei Xia
- State Key Laboratory of Oncology in South China, Collaborative Innovation Center for Cancer Medicine, Guangdong Key Laboratory of Nasopharyngeal Carcinoma Diagnosis and Therapy, Sun Yat-sen University Cancer Center, Guangzhou, 510060, People's Republic of China
| | - Bo Li
- Department of Biochemistry and Molecular Biology, Zhongshan School of Medicine, Sun Yat-Sen University, Guangzhou, 510080, People's Republic of China.,RNA Biomedical Institute, Sun Yat-Sen Memorial Hospital, Sun Yat-Sen University, Guangzhou, 510120, People's Republic of China
| | - Xiao-Jun Xia
- State Key Laboratory of Oncology in South China, Collaborative Innovation Center for Cancer Medicine, Guangdong Key Laboratory of Nasopharyngeal Carcinoma Diagnosis and Therapy, Sun Yat-sen University Cancer Center, Guangzhou, 510060, People's Republic of China
| | - Wei-Hua Jia
- State Key Laboratory of Oncology in South China, Collaborative Innovation Center for Cancer Medicine, Guangdong Key Laboratory of Nasopharyngeal Carcinoma Diagnosis and Therapy, Sun Yat-sen University Cancer Center, Guangzhou, 510060, People's Republic of China
| | - Jian-Jun Liu
- Human Genetics, Genome Institute of Singapore, Agency for Science, Technology, and Research, Singapore, 138672, Singapore
| | - Alan Soo-Beng Khoo
- Molecular Pathology Unit, Cancer Research Centre, Institute for Medical Research, 50603, Kuala Lumpur, Malaysia
| | - Taisei Mushiroda
- Laboratory for International Alliance on Genomic Research, RIKEN Center for Integrative Medical Sciences, Yokohama, 230-0045, Japan
| | - Ching-Ching Ng
- Institute of Biological Sciences, Faculty of Science, University of Malaya, 50603, Kuala Lumpur, Malaysia.
| | - Wen-Hui Su
- Department of Biomedical Sciences, Graduate Institute of Biomedical Sciences, College of Medicine, Chang Gung Molecular Medicine Research Center, Chang Gung University, Taoyuan, 333, Taiwan. .,Department of Otolaryngology, Chang Gung Memorial Hospital, Linkou, Taoyuan, 333, Taiwan.
| | - Yi-Xin Zeng
- State Key Laboratory of Oncology in South China, Collaborative Innovation Center for Cancer Medicine, Guangdong Key Laboratory of Nasopharyngeal Carcinoma Diagnosis and Therapy, Sun Yat-sen University Cancer Center, Guangzhou, 510060, People's Republic of China.
| | - Jin-Xin Bei
- State Key Laboratory of Oncology in South China, Collaborative Innovation Center for Cancer Medicine, Guangdong Key Laboratory of Nasopharyngeal Carcinoma Diagnosis and Therapy, Sun Yat-sen University Cancer Center, Guangzhou, 510060, People's Republic of China. .,Center for Precision Medicine, Sun Yat-sen University, Guangzhou, 510080, People's Republic of China.
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26
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Tam V, Turcotte M, Meyre D. Established and emerging strategies to crack the genetic code of obesity. Obes Rev 2019; 20:212-240. [PMID: 30353704 DOI: 10.1111/obr.12770] [Citation(s) in RCA: 17] [Impact Index Per Article: 2.8] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 06/05/2018] [Revised: 08/27/2018] [Accepted: 08/28/2018] [Indexed: 12/11/2022]
Abstract
Tremendous progress has been made in the genetic elucidation of obesity over the past two decades, driven largely by technological, methodological and organizational innovations. Current strategies for identifying obesity-predisposing loci/genes, including cytogenetics, linkage analysis, homozygosity mapping, admixture mapping, candidate gene studies, genome-wide association studies, custom genotyping arrays, whole-exome sequencing and targeted exome sequencing, have achieved differing levels of success, and the identified loci in aggregate explain only a modest fraction of the estimated heritability of obesity. This review outlines the successes and limitations of these approaches and proposes novel strategies, including the use of exceptionally large sample sizes, the study of diverse ethnic groups and deep phenotypes and the application of innovative methods and study designs, to identify the remaining obesity-predisposing genes. The use of both established and emerging strategies has the potential to crack the genetic code of obesity in the not-too-distant future. The resulting knowledge is likely to yield improvements in obesity prediction, prevention and care.
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Affiliation(s)
- V Tam
- Department of Health Research Methods, Evidence, and Impact, McMaster University, Hamilton, ON, Canada
| | - M Turcotte
- Department of Health Research Methods, Evidence, and Impact, McMaster University, Hamilton, ON, Canada
| | - D Meyre
- Department of Health Research Methods, Evidence, and Impact, McMaster University, Hamilton, ON, Canada.,Department of Pathology and Molecular Medicine, McMaster University, Hamilton, ON, Canada
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27
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Corella D, Coltell O, Portolés O, Sotos-Prieto M, Fernández-Carrión R, Ramirez-Sabio JB, Zanón-Moreno V, Mattei J, Sorlí JV, Ordovas JM. A Guide to Applying the Sex-Gender Perspective to Nutritional Genomics. Nutrients 2018; 11:E4. [PMID: 30577445 PMCID: PMC6357147 DOI: 10.3390/nu11010004] [Citation(s) in RCA: 32] [Impact Index Per Article: 4.6] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/29/2018] [Revised: 12/14/2018] [Accepted: 12/18/2018] [Indexed: 02/07/2023] Open
Abstract
Precision nutrition aims to make dietary recommendations of a more personalized nature possible, to optimize the prevention or delay of a disease and to improve health. Therefore, the characteristics (including sex) of an individual have to be taken into account as well as a series of omics markers. The results of nutritional genomics studies are crucial to generate the evidence needed so that precision nutrition can be applied. Although sex is one of the fundamental variables for making recommendations, at present, the nutritional genomics studies undertaken have not analyzed, systematically and with a gender perspective, the heterogeneity/homogeneity in gene-diet interactions on the different phenotypes studied, thus there is little information available on this issue and needs to be improved. Here we argue for the need to incorporate the gender perspective in nutritional genomics studies, present the general context, analyze the differences between sex and gender, as well as the limitations to measuring them and to detecting specific sex-gene or sex-phenotype associations, both at the specific gene level or in genome-wide-association studies. We analyzed the main sex-specific gene-diet interactions published to date and their main limitations and present guidelines with recommendations to be followed when undertaking new nutritional genomics studies incorporating the gender perspective.
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Affiliation(s)
- Dolores Corella
- CIBER Fisiopatología de la Obesidad y Nutrición, Instituto de Salud Carlos III, 28029 Madrid, Spain.
- Department of Preventive Medicine and Public Health, School of Medicine, University of Valencia, 46010 Valencia, Spain.
| | - Oscar Coltell
- CIBER Fisiopatología de la Obesidad y Nutrición, Instituto de Salud Carlos III, 28029 Madrid, Spain.
- Department of Computer Languages and Systems, Universitat Jaume I, 12071 Castellón, Spain.
| | - Olga Portolés
- CIBER Fisiopatología de la Obesidad y Nutrición, Instituto de Salud Carlos III, 28029 Madrid, Spain.
- Department of Preventive Medicine and Public Health, School of Medicine, University of Valencia, 46010 Valencia, Spain.
| | - Mercedes Sotos-Prieto
- School of Applied Health Sciences and Wellness, Ohio University, Athens, OH 45701, USA.
- Department of Nutrition, Harvard T.H. Chan School of Public Health, Boston, MA 02115, USA.
- Department of Environmental Health, Harvard T.H. Chan School of Public Health, Boston, MA 02115, USA.
| | - Rebeca Fernández-Carrión
- CIBER Fisiopatología de la Obesidad y Nutrición, Instituto de Salud Carlos III, 28029 Madrid, Spain.
- Department of Preventive Medicine and Public Health, School of Medicine, University of Valencia, 46010 Valencia, Spain.
| | | | - Vicente Zanón-Moreno
- Ophthalmology Research Unit "Santiago Grisolia", Dr. Peset University Hospital, 46017 Valencia, Spain.
- Red Temática de Investigación Cooperativa OftaRed, Instituto de Salud Carlos III, 28029 Madrid, Spain.
| | - Josiemer Mattei
- Department of Nutrition, Harvard T.H. Chan School of Public Health, Boston, MA 02115, USA.
| | - José V Sorlí
- CIBER Fisiopatología de la Obesidad y Nutrición, Instituto de Salud Carlos III, 28029 Madrid, Spain.
- Department of Preventive Medicine and Public Health, School of Medicine, University of Valencia, 46010 Valencia, Spain.
| | - Jose M Ordovas
- Nutrition and Genomics Laboratory, JM-USDA Human Nutrition Research Center on Aging at Tufts University, Boston, MA 02111 USA.
- Department of Cardiovascular Epidemiology and Population Genetics, Centro Nacional de Investigaciones Cardiovasculares (CNIC), 28029 Madrid, Spain.
- IMDEA Alimentación, 28049 Madrid, Spain.
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28
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Abstract
Fundamental differences exist between males and females, encompassing anatomy, physiology, behaviour, and genetics. Such differences undoubtedly play a part in the well documented, yet poorly understood, disparity in disease susceptibility between the sexes. Although traditionally attributed to gonadal sex hormone effects, recent work has begun to shed more light on the contribution of genetics - and in particular the sex chromosomes - to these sexual dimorphisms. Here, we explore the accumulating evidence for a significant genetic component to mammalian sexual dimorphism through the paradigm of sex chromosome evolution. The differences between the extant X and Y chromosomes, at both a sequence and regulatory level, arose across 166 million years. A functional result of these differences is cell autonomous sexual dimorphism. By understanding the process that changed a pair of homologous ancestral autosomes into the extant mammalian X and Y, we believe it easier to consider the mechanisms that may contribute to hormone-independent male-female differences. We highlight key roles for genes with homologues present on both sex chromosomes, where the X-linked copy escapes X chromosome inactivation. Finally, we summarise current experimental paradigms and suggest areas for developments to further increase our understanding of cell autonomous sexual dimorphism in the context of health and disease.
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Affiliation(s)
- Daniel M Snell
- Sex Chromosome Biology Laboratory, The Francis Crick Institute, 1 Midland Road, London NW1 1AT, UK
| | - James M A Turner
- Sex Chromosome Biology Laboratory, The Francis Crick Institute, 1 Midland Road, London NW1 1AT, UK.
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29
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Pirinen M, Benner C, Marttinen P, Järvelin MR, Rivas MA, Ripatti S. biMM: efficient estimation of genetic variances and covariances for cohorts with high-dimensional phenotype measurements. Bioinformatics 2018; 33:2405-2407. [PMID: 28369165 PMCID: PMC5860115 DOI: 10.1093/bioinformatics/btx166] [Citation(s) in RCA: 9] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/16/2016] [Accepted: 03/22/2017] [Indexed: 11/21/2022] Open
Abstract
Summary Genetic research utilizes a decomposition of trait variances and covariances into genetic and environmental parts. Our software package biMM is a computationally efficient implementation of a bivariate linear mixed model for settings where hundreds of traits have been measured on partially overlapping sets of individuals. Availability and Implementation Implementation in R freely available at www.iki.fi/mpirinen. Supplementary information Supplementary data are available at Bioinformatics online.
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Affiliation(s)
- Matti Pirinen
- Institute for Molecular Medicine Finland (FIMM), University of Helsinki, Helsinki, Finland.,Helsinki Institute for Information Technology HIIT and Department of Mathematics and Statistics, University of Helsinki, Helsinki, Finland.,Department of Public Health, University of Helsinki, Helsinki, Finland
| | - Christian Benner
- Institute for Molecular Medicine Finland (FIMM), University of Helsinki, Helsinki, Finland.,Department of Public Health, University of Helsinki, Helsinki, Finland
| | - Pekka Marttinen
- Helsinki Institute for Information Technology HIIT and Department of Mathematics and Statistics, University of Helsinki, Helsinki, Finland.,Helsinki Institute for Information Technology HIIT and Department of Computer Science, Aalto University, Espoo, Finland
| | - Marjo-Riitta Järvelin
- Biocenter Oulu, University of Oulu, Oulu, Finland.,Department of Epidemiology and Biostatistics, MRC-PHE Centre for Environment and Health, School of Public Health, Imperial College London, London, UK.,Center for Life Course and Systems Epidemiology, Faculty of Medicine, University of Oulu, Oulu, Finland.,Unit of Primary Care, Oulu University Hospital, Oulu, Finland
| | - Manuel A Rivas
- Department of Biomedical Data Science, Stanford University, Stanford, CA, USA
| | - Samuli Ripatti
- Institute for Molecular Medicine Finland (FIMM), University of Helsinki, Helsinki, Finland.,Department of Public Health, University of Helsinki, Helsinki, Finland
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30
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Genetic contribution to waist-to-hip ratio in Mexican children and adolescents based on 12 loci validated in European adults. Int J Obes (Lond) 2018; 43:13-22. [PMID: 29777226 DOI: 10.1038/s41366-018-0055-8] [Citation(s) in RCA: 8] [Impact Index Per Article: 1.1] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 09/17/2017] [Revised: 01/10/2018] [Accepted: 02/09/2018] [Indexed: 12/19/2022]
Abstract
BACKGROUND/OBJECTIVES The prevalence of abdominal obesity in Mexican children has risen dramatically in the past decade. Genome-wide association studies (GWAS) for waist-to-hip ratio (WHR) performed predominantly in European descent adult populations have identified multiple single-nucleotide polymorphisms (SNPs) with larger effects in women. The contribution of these SNPs to WHR in non-European children is unknown. SUBJECTS/METHODS Mexican children and adolescents (N = 1421, 5-17 years) were recruited in Mexico City. Twelve GWAS SNPs were genotyped using TaqMan Open Array and analyzed individually and as a gene score (GS). RESULTS Mexican boys and girls displayed 2.81 ± 0.29 and 3.10 ± 0.31 WHR standard deviations higher than children and adolescents from the United States. WHR was positively associated with TG (β = 0.733 ± 0.190, P = 1.1 × 10-4) and LDL-C (β = 0.491 ± 0.203, P = 1.6 × 10-2), and negatively associated with HDL-C (β = -0.652 ± 0.195, P = 8.0 × 10-4), independently of body mass index. The effect allele frequency (EAF) of 8 of 12 (67%) SNPs differed significantly (P < 4.17 × 10-3) in Mexican children and European adults, with no evidence of effect allele enrichment in both populations (4 depleted and 4 enriched; binomial test, P = 1). Ten out of 12 SNPs (83.3%) had effects that were directionally consistent with those reported in GWAS (P = 0.04). HOXC13 rs1443512 displayed the best fit when modeled recessively, and was significantly associated with WHR under a recessive mode of inheritance (β = 0.140 ± 0.06, P = 2.3 × 10-2). Significant interactions with sex were also observed for HOXC13 rs1443512 and the GS on WHR (P = 2.2 × 10-2 and 1.2 × 10-2, respectively). HOXC13 rs1443512 (β = 0.022 ± 0.012, P = 4.7 × 10-2) and the GS (β = 0.007 ± 0.003, P = 7.0 × 10-3) were significantly associated with WHR in girls only. CONCLUSIONS This study demonstrates that Mexican children are at high risk for abdominal obesity and detrimental lipid profiles. Our data support a partial transferability of sex-specific European GWAS WHR association signals in children and adolescents from the admixed Mexican population.
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31
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Kouvari M, Yannakoulia M, Souliotis K, Panagiotakos DB. Challenges in Sex- and Gender-Centered Prevention and Management of Cardiovascular Disease: Implications of Genetic, Metabolic, and Environmental Paths. Angiology 2018; 69:843-853. [PMID: 29430964 DOI: 10.1177/0003319718756732] [Citation(s) in RCA: 18] [Impact Index Per Article: 2.6] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/29/2022]
Abstract
The recognition of cardiovascular disease (CVD) as a "male" privilege has been a commonly held concept. However, emerging data describe another reality. Heterogeneities have been convincingly demonstrated regarding CVD manifestations, risk factor burden, and prognosis between males and females. The aim of the present narrative review was to highlight sex- and gender-related discrepancies in primary and secondary CVD prevention, underscoring plausible underlying mechanisms. Manifestation of CVD in women is characterized by atypical symptoms/signs and inadequately studied pathophysiology features challenging accurate diagnosis and effective treatment. Regarding CVD risk assessment, the burden and effect size of conventional, novel, and female-specific risk factors needs better clarification. Hitherto outcomes are nonconsistent, while most importantly, the interpretation of the attendant metabolic paths remains a challenge; the interactions among genetic, metabolic, and environmental factors are of high complexity regulated by genomic and nongenomic sex hormones effects. To deal with these key points, the National Institutes of Health currently calls upon investigators to provide a sex- and gender-specific reporting in all health research hypotheses. The implementation of high-quality studies addressing these issues is an imperative need to maximize cost-effectiveness in prevention and management strategies.
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Affiliation(s)
- Matina Kouvari
- 1 Department of Nutrition and Dietetics, School of Health Science and Education, Harokopio University, Athens, Greece
| | - Mary Yannakoulia
- 1 Department of Nutrition and Dietetics, School of Health Science and Education, Harokopio University, Athens, Greece
| | - Kyriakos Souliotis
- 2 Faculty of Social Sciences, University of Peloponnese, Korinthos, Greece
| | - Demosthenes B Panagiotakos
- 1 Department of Nutrition and Dietetics, School of Health Science and Education, Harokopio University, Athens, Greece
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32
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33
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Re-analysis of public genetic data reveals a rare X-chromosomal variant associated with type 2 diabetes. Nat Commun 2018; 9:321. [PMID: 29358691 PMCID: PMC5778074 DOI: 10.1038/s41467-017-02380-9] [Citation(s) in RCA: 73] [Impact Index Per Article: 10.4] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/13/2017] [Accepted: 11/24/2017] [Indexed: 12/20/2022] Open
Abstract
The reanalysis of existing GWAS data represents a powerful and cost-effective opportunity to gain insights into the genetics of complex diseases. By reanalyzing publicly available type 2 diabetes (T2D) genome-wide association studies (GWAS) data for 70,127 subjects, we identify seven novel associated regions, five driven by common variants (LYPLAL1, NEUROG3, CAMKK2, ABO, and GIP genes), one by a low-frequency (EHMT2), and one driven by a rare variant in chromosome Xq23, rs146662075, associated with a twofold increased risk for T2D in males. rs146662075 is located within an active enhancer associated with the expression of Angiotensin II Receptor type 2 gene (AGTR2), a modulator of insulin sensitivity, and exhibits allelic specific activity in muscle cells. Beyond providing insights into the genetics and pathophysiology of T2D, these results also underscore the value of reanalyzing publicly available data using novel genetic resources and analytical approaches. Genome-wide association studies have uncovered several loci associated with diabetes risk. Here, the authors reanalyse public type 2 diabetes GWAS data to fine map 50 known loci and identify seven new ones, including one near ATGR2 on the X-chromosome that doubles the risk of diabetes in men.
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34
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Márquez-Luna C, Loh PR, Price AL. Multiethnic polygenic risk scores improve risk prediction in diverse populations. Genet Epidemiol 2017; 41:811-823. [PMID: 29110330 PMCID: PMC5726434 DOI: 10.1002/gepi.22083] [Citation(s) in RCA: 204] [Impact Index Per Article: 25.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/12/2017] [Revised: 08/16/2017] [Accepted: 08/30/2017] [Indexed: 01/04/2023]
Abstract
Methods for genetic risk prediction have been widely investigated in recent years. However, most available training data involves European samples, and it is currently unclear how to accurately predict disease risk in other populations. Previous studies have used either training data from European samples in large sample size or training data from the target population in small sample size, but not both. Here, we introduce a multiethnic polygenic risk score that combines training data from European samples and training data from the target population. We applied this approach to predict type 2 diabetes (T2D) in a Latino cohort using both publicly available European summary statistics in large sample size (Neff = 40k) and Latino training data in small sample size (Neff = 8k). Here, we attained a >70% relative improvement in prediction accuracy (from R2 = 0.027 to 0.047) compared to methods that use only one source of training data, consistent with large relative improvements in simulations. We observed a systematically lower load of T2D risk alleles in Latino individuals with more European ancestry, which could be explained by polygenic selection in ancestral European and/or Native American populations. We predict T2D in a South Asian UK Biobank cohort using European (Neff = 40k) and South Asian (Neff = 16k) training data and attained a >70% relative improvement in prediction accuracy, and application to predict height in an African UK Biobank cohort using European (N = 113k) and African (N = 2k) training data attained a 30% relative improvement. Our work reduces the gap in polygenic risk prediction accuracy between European and non-European target populations.
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Affiliation(s)
- Carla Márquez-Luna
- Department of Biostatistics, Harvard T. H. Chan School of Public Health, Boston, Massachusetts, United States of America
| | - Po-Ru Loh
- Department of Epidemiology, Harvard T. H. Chan School of Public Health, Boston, Massachusetts, United States of America
- Program in Medical and Population Genetics, Broad Institute of Harvard and MIT, Cambridge, Massachusetts, United States of America
| | - Alkes L Price
- Department of Biostatistics, Harvard T. H. Chan School of Public Health, Boston, Massachusetts, United States of America
- Department of Epidemiology, Harvard T. H. Chan School of Public Health, Boston, Massachusetts, United States of America
- Program in Medical and Population Genetics, Broad Institute of Harvard and MIT, Cambridge, Massachusetts, United States of America
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35
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Tukiainen T, Villani AC, Yen A, Rivas MA, Marshall JL, Satija R, Aguirre M, Gauthier L, Fleharty M, Kirby A, Cummings BB, Castel SE, Karczewski KJ, Aguet F, Byrnes A, GTEx Consortium, Lappalainen T, Regev A, Ardlie KG, Hacohen N, MacArthur DG. Landscape of X chromosome inactivation across human tissues. Nature 2017; 550:244-248. [PMID: 29022598 PMCID: PMC5685192 DOI: 10.1038/nature24265] [Citation(s) in RCA: 733] [Impact Index Per Article: 91.6] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/30/2016] [Accepted: 09/28/2017] [Indexed: 12/16/2022]
Abstract
X chromosome inactivation (XCI) silences transcription from one of the two X chromosomes in female mammalian cells to balance expression dosage between XX females and XY males. XCI is, however, incomplete in humans: up to one-third of X-chromosomal genes are expressed from both the active and inactive X chromosomes (Xa and Xi, respectively) in female cells, with the degree of 'escape' from inactivation varying between genes and individuals. The extent to which XCI is shared between cells and tissues remains poorly characterized, as does the degree to which incomplete XCI manifests as detectable sex differences in gene expression and phenotypic traits. Here we describe a systematic survey of XCI, integrating over 5,500 transcriptomes from 449 individuals spanning 29 tissues from GTEx (v6p release) and 940 single-cell transcriptomes, combined with genomic sequence data. We show that XCI at 683 X-chromosomal genes is generally uniform across human tissues, but identify examples of heterogeneity between tissues, individuals and cells. We show that incomplete XCI affects at least 23% of X-chromosomal genes, identify seven genes that escape XCI with support from multiple lines of evidence and demonstrate that escape from XCI results in sex biases in gene expression, establishing incomplete XCI as a mechanism that is likely to introduce phenotypic diversity. Overall, this updated catalogue of XCI across human tissues helps to increase our understanding of the extent and impact of the incompleteness in the maintenance of XCI.
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Affiliation(s)
- Taru Tukiainen
- Analytic and Translational Genetics Unit, Massachusetts General Hospital, Boston, MA 02114, USA
- Broad Institute of MIT and Harvard, Cambridge, MA 02142, USA
| | - Alexandra-Chloé Villani
- Broad Institute of MIT and Harvard, Cambridge, MA 02142, USA
- Center for Immunology and Inflammatory Diseases, Massachusetts General Hospital, Charlestown, MA 02129, USA
| | - Angela Yen
- Broad Institute of MIT and Harvard, Cambridge, MA 02142, USA
- Computer Science and Artificial Intelligence Laboratory, Massachusetts Institute of Technology, Cambridge, MA 02139, USA
| | - Manuel A. Rivas
- Analytic and Translational Genetics Unit, Massachusetts General Hospital, Boston, MA 02114, USA
- Broad Institute of MIT and Harvard, Cambridge, MA 02142, USA
- Department of Biomedical Data Science, Stanford University, Stanford, CA 94305, USA
| | - Jamie L. Marshall
- Analytic and Translational Genetics Unit, Massachusetts General Hospital, Boston, MA 02114, USA
- Broad Institute of MIT and Harvard, Cambridge, MA 02142, USA
| | - Rahul Satija
- Broad Institute of MIT and Harvard, Cambridge, MA 02142, USA
- New York Genome Center, New York, NY 10013, USA
- Center for Genomics and Systems Biology, Department of Biology, New York University, New York, NY 10003, USA
| | - Matt Aguirre
- Analytic and Translational Genetics Unit, Massachusetts General Hospital, Boston, MA 02114, USA
- Broad Institute of MIT and Harvard, Cambridge, MA 02142, USA
| | - Laura Gauthier
- Analytic and Translational Genetics Unit, Massachusetts General Hospital, Boston, MA 02114, USA
- Broad Institute of MIT and Harvard, Cambridge, MA 02142, USA
| | - Mark Fleharty
- Broad Institute of MIT and Harvard, Cambridge, MA 02142, USA
| | - Andrew Kirby
- Analytic and Translational Genetics Unit, Massachusetts General Hospital, Boston, MA 02114, USA
- Broad Institute of MIT and Harvard, Cambridge, MA 02142, USA
| | - Beryl B. Cummings
- Analytic and Translational Genetics Unit, Massachusetts General Hospital, Boston, MA 02114, USA
- Broad Institute of MIT and Harvard, Cambridge, MA 02142, USA
| | - Stephane E. Castel
- New York Genome Center, New York, NY 10013, USA
- Department of Systems Biology, Columbia University, New York, NY 10032, USA
| | - Konrad J. Karczewski
- Analytic and Translational Genetics Unit, Massachusetts General Hospital, Boston, MA 02114, USA
- Broad Institute of MIT and Harvard, Cambridge, MA 02142, USA
| | - François Aguet
- Broad Institute of MIT and Harvard, Cambridge, MA 02142, USA
| | - Andrea Byrnes
- Analytic and Translational Genetics Unit, Massachusetts General Hospital, Boston, MA 02114, USA
- Broad Institute of MIT and Harvard, Cambridge, MA 02142, USA
| | | | - Tuuli Lappalainen
- New York Genome Center, New York, NY 10013, USA
- Department of Systems Biology, Columbia University, New York, NY 10032, USA
| | - Aviv Regev
- Broad Institute of MIT and Harvard, Cambridge, MA 02142, USA
- Department of Biology, Massachusetts Institute of Technology, Cambridge, MA 02139, USA
| | | | - Nir Hacohen
- Broad Institute of MIT and Harvard, Cambridge, MA 02142, USA
- Center for Immunology and Inflammatory Diseases, Massachusetts General Hospital, Charlestown, MA 02129, USA
| | - Daniel G. MacArthur
- Analytic and Translational Genetics Unit, Massachusetts General Hospital, Boston, MA 02114, USA
- Broad Institute of MIT and Harvard, Cambridge, MA 02142, USA
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36
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A new approach to chromosome-wide analysis of X-linked markers identifies new associations in Asian and European case-parent triads of orofacial clefts. PLoS One 2017; 12:e0183772. [PMID: 28877219 PMCID: PMC5587310 DOI: 10.1371/journal.pone.0183772] [Citation(s) in RCA: 15] [Impact Index Per Article: 1.9] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/19/2017] [Accepted: 08/10/2017] [Indexed: 11/19/2022] Open
Abstract
Background GWAS discoveries on the X-chromosome are underrepresented in the literature primarily because the analytical tools that have been applied were originally designed for autosomal markers. Our objective here is to employ a new robust and flexible tool for chromosome-wide analysis of X-linked markers in complex traits. Orofacial clefts are good candidates for such analysis because of the consistently observed excess of females with cleft palate only (CPO) and excess of males with cleft lip with or without cleft palate (CL/P). Methods Genotypes for 14,486 X-chromosome SNPs in 1,291 Asian and 1,118 European isolated cleft triads were available from a previously published GWAS. The R-package HAPLIN enables genome-wide–level analyses as well as statistical power simulations for a range of biologic scenarios. We analyzed isolated CL/P and isolated CPO for each ethnicity in HAPLIN, using a sliding-window approach to haplotype analysis and two different statistical models, with and without X-inactivation in females. Results There was a larger number of associations in the Asian versus the European sample, and similar to previous reports that have analyzed the same GWAS dataset using different methods, we identified associations with EFNB1/PJA1 and DMD. In addition, new associations were detected with several other genes, among which KLHL4, TBX22, CPXCR1 and BCOR were noteworthy because of their roles in clefting syndromes. A few of the associations were only detected by one particular X-inactivation model, whereas a few others were only detected in one sex. Discussion/Conclusion We found new support for the involvement of X-linked variants in isolated clefts. The associations were specific for ethnicity, sex and model parameterization, highlighting the need for flexible tools that are capable of detecting and estimating such effects. Further efforts are needed to verify and elucidate the potential roles of EFNB1/PJA1, KLHL4, TBX22, CPXCR1 and BCOR in isolated clefts.
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37
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Li N, Zhao L, Li J, Ding Y, Shen Y, Huang X, Wang X, Wang J. Turner syndrome caused by rare complex structural abnormalities involving chromosome X. Exp Ther Med 2017; 14:2265-2270. [PMID: 28962153 PMCID: PMC5609171 DOI: 10.3892/etm.2017.4756] [Citation(s) in RCA: 3] [Impact Index Per Article: 0.4] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/18/2016] [Accepted: 04/10/2017] [Indexed: 01/15/2023] Open
Abstract
Turner syndrome (TS) is a phenotypic heterogeneous genetic disorder caused by the loss of an X-chromosome or X-structural abnormalities in the X-chromosome, and affects approximately 1 in every 2,500 females. The affected individuals may develop diverse clinical features, including short stature, ovarian dysgenesis, skeletal dysplasia, facial abnormalities and other disorders. A constitutional karyotype of 45, X accounts for nearly 50% of TS patients, while X-mosaicism and other X-chromosomal structural abnormalities, including deletions, duplications, ring, isodicentric chromosomes, inversions and translocations, have been reported in other cases. The present study reports the results of chromosome microarray analysis (CMA) in two Chinese female TS patients with idiosyncratic karyotypes. The first patient had a karyotype of 46, X, der(X), and the CMA results demonstrated that the derivative chromosome was an abnormal X-chromosome that consisted of three deletions (Xp21.3-p11.23, Xp11.1-q13.1 and Xq21.31-q28), as well as three duplications (Xp22.33-p21.3, Xp11.23-p11.1 and Xq13.1-q21.31). The karyotype of the second patient was 46, X, der(X) t(X;?)(q 22.1;?),inv(11)(q13.5q21), while CMA revealed an Xq21.2-q27.1 duplication and an Xq27.2-q28 deletion. In conclusion, the current study performed genotype-phenotype correlation analysis in two patients and provided novel insight of the genotype of TS.
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Affiliation(s)
- Niu Li
- Department of Medical Genetics, Shanghai Children's Medical Center, Shanghai Jiaotong University School of Medicine, Shanghai 200127, P.R. China
- Institute of Pediatric Translational Medicine, Shanghai Children's Medical Center, Shanghai Jiaotong University School of Medicine, Shanghai 200127, P.R. China
| | - Li Zhao
- Department of Internal Medicine, Shanghai Children's Medical Center, Shanghai Jiaotong University School of Medicine, Shanghai 200127, P.R. China
| | - Juan Li
- Department of Internal Medicine, Shanghai Children's Medical Center, Shanghai Jiaotong University School of Medicine, Shanghai 200127, P.R. China
| | - Yu Ding
- Department of Internal Medicine, Shanghai Children's Medical Center, Shanghai Jiaotong University School of Medicine, Shanghai 200127, P.R. China
| | - Yongnian Shen
- Department of Internal Medicine, Shanghai Children's Medical Center, Shanghai Jiaotong University School of Medicine, Shanghai 200127, P.R. China
| | - Xiaodong Huang
- Department of Internal Medicine, Shanghai Children's Medical Center, Shanghai Jiaotong University School of Medicine, Shanghai 200127, P.R. China
| | - Xiumin Wang
- Department of Medical Genetics, Shanghai Children's Medical Center, Shanghai Jiaotong University School of Medicine, Shanghai 200127, P.R. China
- Department of Internal Medicine, Shanghai Children's Medical Center, Shanghai Jiaotong University School of Medicine, Shanghai 200127, P.R. China
| | - Jian Wang
- Department of Medical Genetics, Shanghai Children's Medical Center, Shanghai Jiaotong University School of Medicine, Shanghai 200127, P.R. China
- Institute of Pediatric Translational Medicine, Shanghai Children's Medical Center, Shanghai Jiaotong University School of Medicine, Shanghai 200127, P.R. China
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38
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Xu SS, Ren X, Yang GL, Xie XL, Zhao YX, Zhang M, Shen ZQ, Ren YL, Gao L, Shen M, Kantanen J, Li MH. Genome-wide association analysis identifies the genetic basis of fat deposition in the tails of sheep (Ovis aries). Anim Genet 2017; 48:560-569. [PMID: 28677334 DOI: 10.1111/age.12572] [Citation(s) in RCA: 39] [Impact Index Per Article: 4.9] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Accepted: 04/25/2017] [Indexed: 12/13/2022]
Abstract
Fat-tailed sheep (Ovis aries) can survive in harsh environments and satisfy human's intake of dietary fat. However, the animals require more feed, which increases the cost of farming. Thus, most farmers currently prefer thin-tailed, short-tailed or docked sheep. To date, the molecular mechanism of the formation of fat tails in sheep has not been completely elucidated. Here, we conducted a genome-wide association study using phenotypes and genotypes (the Ovine Infinium HD SNP BeadChip genotype data) of two breeds of contrasting tail types (78 Small-tailed and 78 Large-tailed Han sheep breeds) to identify functional genes and variants associated with fat deposition. We identified four significantly (rs416433540, rs409848439, rs408118325 and rs402128848) and three approximately associated autosomal SNPs (rs401248376, rs402445895 and rs416201901). Gene annotation indicated that the surrounding genes (CREB1, STEAP4, CTBP1 and RIP140, also known as NRIP1) function in lipid storage or fat cell regulation. Furthermore, through an X-chromosome-wide association analysis, we detected significantly associated SNPs in the OARX: 88-89 Mb region, which could be a strong candidate genomic region for fat deposition in tails of sheep. Our results represent a new genomic resource for sheep genetics and breeding. In addition, the findings provide novel insights into genetic mechanisms of fat deposition in the tail of sheep and other mammals.
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Affiliation(s)
- S-S Xu
- CAS Key Laboratory of Animal Ecology and Conservation Biology, Institute of Zoology, Chinese Academy of Sciences (CAS), Beijing, 100101, China.,University of Chinese Academy of Sciences (UCAS), Beijing, 100049, China
| | - X Ren
- CAS Key Laboratory of Animal Ecology and Conservation Biology, Institute of Zoology, Chinese Academy of Sciences (CAS), Beijing, 100101, China.,Annoroad Gene Technology Co. Ltd, Beijing, 100176, China
| | - G-L Yang
- CAS Key Laboratory of Animal Ecology and Conservation Biology, Institute of Zoology, Chinese Academy of Sciences (CAS), Beijing, 100101, China.,Department of Life Sciences, Shangqiu Normal University, Shangqiu, 476000, China
| | - X-L Xie
- CAS Key Laboratory of Animal Ecology and Conservation Biology, Institute of Zoology, Chinese Academy of Sciences (CAS), Beijing, 100101, China.,University of Chinese Academy of Sciences (UCAS), Beijing, 100049, China
| | - Y-X Zhao
- CAS Key Laboratory of Animal Ecology and Conservation Biology, Institute of Zoology, Chinese Academy of Sciences (CAS), Beijing, 100101, China.,University of Chinese Academy of Sciences (UCAS), Beijing, 100049, China
| | - M Zhang
- CAS Key Laboratory of Animal Ecology and Conservation Biology, Institute of Zoology, Chinese Academy of Sciences (CAS), Beijing, 100101, China.,School of Life Sciences, University of Science and Technology of China, Hefei, 230027, China
| | - Z-Q Shen
- Shandong Binzhou Academy of Animal Science and Veterinary Medicine, Binzhou, 256600, China
| | - Y-L Ren
- Shandong Binzhou Academy of Animal Science and Veterinary Medicine, Binzhou, 256600, China
| | - L Gao
- Institute of Animal Husbandry and Veterinary Medicine, Xinjiang Academy of Agricultural and Reclamation Sciences, Shihezi, 832000, China.,State Key Laboratory of Sheep Genetic Improvement and Healthy Breeding, Xinjiang Academy of Agricultural and Reclamation Sciences, Shihezi, 832000, China
| | - M Shen
- Institute of Animal Husbandry and Veterinary Medicine, Xinjiang Academy of Agricultural and Reclamation Sciences, Shihezi, 832000, China.,State Key Laboratory of Sheep Genetic Improvement and Healthy Breeding, Xinjiang Academy of Agricultural and Reclamation Sciences, Shihezi, 832000, China
| | - J Kantanen
- Green Technology, Natural Resources Institute Finland (Luke), Jokioinen, 31600, Finland.,Department of Environmental and Biological Sciences, University of Eastern Finland, Kuopio, 70211, Finland
| | - M-H Li
- CAS Key Laboratory of Animal Ecology and Conservation Biology, Institute of Zoology, Chinese Academy of Sciences (CAS), Beijing, 100101, China
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39
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Won S, Jung J, Park E, Kim H. Identification of genes related to intramuscular fat content of pigs using genome-wide association study. ASIAN-AUSTRALASIAN JOURNAL OF ANIMAL SCIENCES 2017; 31:157-162. [PMID: 28728355 PMCID: PMC5767496 DOI: 10.5713/ajas.17.0218] [Citation(s) in RCA: 29] [Impact Index Per Article: 3.6] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 03/21/2017] [Revised: 04/18/2017] [Accepted: 06/24/2017] [Indexed: 12/20/2022]
Abstract
OBJECTIVE The aim of this study is to identify single nucleotide polymorphisms (SNPs) and genes related to pig IMF and estimate the heritability of intramuscular fat content (IMF). METHODS Genome-wide association study (GWAS) on 704 inbred Berkshires was performed for IMF. To consider the inbreeding among samples, associations of the SNPs with IMF were tested as random effects in a mixed linear model using the genetic relationship matrix by GEMMA. Significant genes were compared with reported pig IMF quantitative trait loci (QTL) regions and functional classification of the identified genes were also performed. Heritability of IMF was estimated by GCTA tool. RESULTS Total 365 SNPs were found to be significant from a cutoff of p-value <0.01 and the 365 significant SNPs were annotated across 120 genes. Twenty five genes were on pig IMF QTL regions. Bone morphogenetic protein-binding endothelial cell precursor-derived regulator, forkhead box protein O1, ectodysplasin A receptor, ring finger protein 149, cluster of differentiation, tyrosine-protein phosphatase non-receptor type 1, SRY (sex determining region Y)-box 9 (SOX9), MYC proto-oncogene, and macrophage migration inhibitory factor were related to mitogen-activated protein kinase pathway, which regulates the differentiation to adipocytes. These genes and the genes mapped on QTLs could be the candidate genes affecting IMF. Heritability of IMF was estimated as 0.52, which was relatively high, suggesting that a considerable portion of the total variance of IMF is explained by the SNP information. CONCLUSION Our results can contribute to breeding pigs with better IMF and therefore, producing pork with better sensory qualities.
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Affiliation(s)
- Sohyoung Won
- Department of Agricultural Biotechnology and Research Institute of Population Genomics, Seoul National University, Seoul 151-742, Korea
| | - Jaehoon Jung
- Department of Agricultural Biotechnology and Research Institute of Population Genomics, Seoul National University, Seoul 151-742, Korea
| | - Eungwoo Park
- Animal Genomics & Bioinformatics Division, National Institute of Animal Science, Rural Development Administration, Wanju 55365, Korea
| | - Heebal Kim
- Department of Agricultural Biotechnology and Research Institute of Population Genomics, Seoul National University, Seoul 151-742, Korea.,CHO&KIM genomics, Seoul 05836, Korea
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40
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Zhu M, Yan C, Ren C, Huang X, Zhu X, Gu H, Wang M, Wang S, Gao Y, Ji Y, Miao X, Yang M, Chen J, Du J, Huang T, Jiang Y, Dai J, Ma H, Zhou J, Wang Z, Hu Z, Ji G, Zhang Z, Shen H, Shi Y, Jin G. Exome Array Analysis Identifies Variants in SPOCD1 and BTN3A2 That Affect Risk for Gastric Cancer. Gastroenterology 2017; 152:2011-2021. [PMID: 28246015 DOI: 10.1053/j.gastro.2017.02.017] [Citation(s) in RCA: 56] [Impact Index Per Article: 7.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 03/18/2016] [Revised: 02/16/2017] [Accepted: 02/21/2017] [Indexed: 12/21/2022]
Abstract
BACKGROUND & AIMS Several genetic variants have been associated with gastric cancer risk, although these account for only a fraction of cases of gastric cancer. We aimed to identify low-frequency and other genetic variants that determine gastric cancer susceptibility. METHODS We performed exome array analysis of DNA in blood samples from 1113 patients with gastric cancer, collected at hospitals from 2006 to 2010 in China, and 1848 individuals without cancer (controls) undergoing physical examinations. Among 71,290 variants analyzed (including 25,784 common variants), 24 variants were selected and replicated in an analysis of DNA in blood samples from 4687 additional cases of gastric cancer and 5780 controls. We compared expression of candidate genes in tumor vs normal gastric tissues using data from TCGA and performed functional annotation analyses. An immortalized human gastric epithelial cell line (GES1) and 7 human gastric cancer lines were used to express transgenes, knock down gene expression (with small interfering RNAs), disrupt genes (using the CRISPR/Cas9 system), or assess expression of reporter constructs. We measured cell proliferation, colony formation, invasion, and migration, and assessed growth of xenograft tumors in nude mice. RESULTS A low-frequency missense variant rs112754928 in the SPOC domain containing 1 gene (SPOCD1; encoding p.Arg71Trp), at 1p35.2, was reproducibly associated with reduced risk of gastric cancer (odds ratio, 0.56; P = 3.48 × 10-8). SPOCD1 was overexpressed in gastric tumors, and knockout of SPOCD1 reduced gastric cancer cell proliferation, invasive activity, and migration, as well as growth of xenograft tumors in nude mice. We also associated the variant rs1679709 at 6p22.1 with reduced risk for gastric cancer (odds ratio, 0.80; P = 1.17 × 10-13). The protective allele rs1679709-A correlated with the surrounding haplotype rs2799077-T-rs2799079-C, which reduced the enhancer activity of this site to decrease expression of the butyrophilin subfamily 3 member A2 gene (BTN3A2). BTN3A2 is overexpressed in gastric tumors, and deletion of BTN3A2 inhibited proliferation, migration, and invasion of gastric cancer cells. CONCLUSIONS We have associated variants at 1p35.2 and 6p22.1 with gastric cancer risk, indicating a role for SPOCD1 and BTN3A2 in gastric carcinogenesis.
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Affiliation(s)
- Meng Zhu
- Department of Epidemiology and Biostatistics, Jiangsu Key Lab of Cancer Biomarkers, Prevention and Treatment, Collaborative Innovation Center for Cancer Medicine, School of Public Health, Nanjing Medical University, Nanjing, China; State Key Laboratory of Reproductive Medicine, International Joint Research Center for Environment and Human Health, Nanjing Medical University, Nanjing, China
| | - Caiwang Yan
- Department of Epidemiology and Biostatistics, Jiangsu Key Lab of Cancer Biomarkers, Prevention and Treatment, Collaborative Innovation Center for Cancer Medicine, School of Public Health, Nanjing Medical University, Nanjing, China
| | - Chuanli Ren
- Department of Epidemiology and Biostatistics, Jiangsu Key Lab of Cancer Biomarkers, Prevention and Treatment, Collaborative Innovation Center for Cancer Medicine, School of Public Health, Nanjing Medical University, Nanjing, China; Clinical Medical Testing Laboratory, Northern Jiangsu People's Hospital and Clinical Medical College of Yangzhou University, Yangzhou, China
| | - Xiaodan Huang
- Institute of Digestive Endoscopy and Medical Center for Digestive Diseases, Second Affiliated Hospital of Nanjing Medical University, Nanjing, China
| | - Xun Zhu
- Department of Epidemiology and Biostatistics, Jiangsu Key Lab of Cancer Biomarkers, Prevention and Treatment, Collaborative Innovation Center for Cancer Medicine, School of Public Health, Nanjing Medical University, Nanjing, China
| | - Haiyong Gu
- Department of Cardiothoracic Surgery, Affiliated People's Hospital of Jiangsu University, Zhenjiang, China
| | - Meilin Wang
- Department of Environmental Genomics, Jiangsu Key Laboratory of Cancer Biomarkers, Prevention and Treatment, Collaborative Innovation Center for Cancer Medicine, Nanjing Medical University, Nanjing, China
| | - Shouyu Wang
- Department of Molecular Cell Biology and Toxicology, Jiangsu Key Lab of Cancer Biomarkers, Prevention and Treatment, Collaborative Innovation Center for Cancer Medicine, School of Public Health, Nanjing Medical University, Nanjing, China
| | - Yong Gao
- Department of Medical Oncology, The Affiliated Huai'an First People's Hospital of Nanjing Medical University, Huai'an, China
| | - Yong Ji
- Department of Cardiothoracic Surgery, The Affiliated Wuxi People's Hospital of Nanjing Medical University, Wuxi, China
| | - Xiaoping Miao
- MOE Key Lab of Environment and Health, School of Public Health, Tongji Medical College, Huazhong University of Science & Technology, Wuhan, China
| | - Ming Yang
- Shandong Provincial Key Laboratory of Radiation Oncology, Cancer Research Center, Shandong Cancer Hospital Affiliated to Shandong University, Shandong Academy of Medical Sciences, Jinan, China
| | - Jinfei Chen
- Department of Oncology, Nanjing First Hospital, Nanjing Medical University, Nanjing, China
| | - Jiangbo Du
- Department of Epidemiology and Biostatistics, Jiangsu Key Lab of Cancer Biomarkers, Prevention and Treatment, Collaborative Innovation Center for Cancer Medicine, School of Public Health, Nanjing Medical University, Nanjing, China
| | - Tongtong Huang
- Department of Epidemiology and Biostatistics, Jiangsu Key Lab of Cancer Biomarkers, Prevention and Treatment, Collaborative Innovation Center for Cancer Medicine, School of Public Health, Nanjing Medical University, Nanjing, China
| | - Yue Jiang
- Department of Epidemiology and Biostatistics, Jiangsu Key Lab of Cancer Biomarkers, Prevention and Treatment, Collaborative Innovation Center for Cancer Medicine, School of Public Health, Nanjing Medical University, Nanjing, China
| | - Juncheng Dai
- Department of Epidemiology and Biostatistics, Jiangsu Key Lab of Cancer Biomarkers, Prevention and Treatment, Collaborative Innovation Center for Cancer Medicine, School of Public Health, Nanjing Medical University, Nanjing, China
| | - Hongxia Ma
- Department of Epidemiology and Biostatistics, Jiangsu Key Lab of Cancer Biomarkers, Prevention and Treatment, Collaborative Innovation Center for Cancer Medicine, School of Public Health, Nanjing Medical University, Nanjing, China
| | - Jianwei Zhou
- Department of Molecular Cell Biology and Toxicology, Jiangsu Key Lab of Cancer Biomarkers, Prevention and Treatment, Collaborative Innovation Center for Cancer Medicine, School of Public Health, Nanjing Medical University, Nanjing, China
| | - Zhaoming Wang
- Department of Epidemiology and Biostatistics, Jiangsu Key Lab of Cancer Biomarkers, Prevention and Treatment, Collaborative Innovation Center for Cancer Medicine, School of Public Health, Nanjing Medical University, Nanjing, China
| | - Zhibin Hu
- Department of Epidemiology and Biostatistics, Jiangsu Key Lab of Cancer Biomarkers, Prevention and Treatment, Collaborative Innovation Center for Cancer Medicine, School of Public Health, Nanjing Medical University, Nanjing, China
| | - Guozhong Ji
- Institute of Digestive Endoscopy and Medical Center for Digestive Diseases, Second Affiliated Hospital of Nanjing Medical University, Nanjing, China
| | - Zhengdong Zhang
- Department of Environmental Genomics, Jiangsu Key Laboratory of Cancer Biomarkers, Prevention and Treatment, Collaborative Innovation Center for Cancer Medicine, Nanjing Medical University, Nanjing, China
| | - Hongbing Shen
- Department of Epidemiology and Biostatistics, Jiangsu Key Lab of Cancer Biomarkers, Prevention and Treatment, Collaborative Innovation Center for Cancer Medicine, School of Public Health, Nanjing Medical University, Nanjing, China.
| | - Yongyong Shi
- Key Laboratory for the Genetics of Developmental and Neuropsychiatric Disorders, Ministry of Education, Bio-X Institutes, Shanghai Jiao Tong University, Shanghai, China.
| | - Guangfu Jin
- Department of Epidemiology and Biostatistics, Jiangsu Key Lab of Cancer Biomarkers, Prevention and Treatment, Collaborative Innovation Center for Cancer Medicine, School of Public Health, Nanjing Medical University, Nanjing, China; State Key Laboratory of Reproductive Medicine, International Joint Research Center for Environment and Human Health, Nanjing Medical University, Nanjing, China.
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41
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Regitz-Zagrosek V, Kararigas G. Mechanistic Pathways of Sex Differences in Cardiovascular Disease. Physiol Rev 2017; 97:1-37. [PMID: 27807199 DOI: 10.1152/physrev.00021.2015] [Citation(s) in RCA: 472] [Impact Index Per Article: 59.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 02/08/2023] Open
Abstract
Major differences between men and women exist in epidemiology, manifestation, pathophysiology, treatment, and outcome of cardiovascular diseases (CVD), such as coronary artery disease, pressure overload, hypertension, cardiomyopathy, and heart failure. Corresponding sex differences have been studied in a number of animal models, and mechanistic investigations have been undertaken to analyze the observed sex differences. We summarize the biological mechanisms of sex differences in CVD focusing on three main areas, i.e., genetic mechanisms, epigenetic mechanisms, as well as sex hormones and their receptors. We discuss relevant subtypes of sex hormone receptors, as well as genomic and nongenomic, activational and organizational effects of sex hormones. We describe the interaction of sex hormones with intracellular signaling relevant for cardiovascular cells and the cardiovascular system. Sex, sex hormones, and their receptors may affect a number of cellular processes by their synergistic action on multiple targets. We discuss in detail sex differences in organelle function and in biological processes. We conclude that there is a need for a more detailed understanding of sex differences and their underlying mechanisms, which holds the potential to design new drugs that target sex-specific cardiovascular mechanisms and affect phenotypes. The comparison of both sexes may lead to the identification of protective or maladaptive mechanisms in one sex that could serve as a novel therapeutic target in one sex or in both.
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Affiliation(s)
- Vera Regitz-Zagrosek
- Institute of Gender in Medicine & Center for Cardiovascular Research, Charite University Hospital, and DZHK (German Centre for Cardiovascular Research), Berlin, Germany
| | - Georgios Kararigas
- Institute of Gender in Medicine & Center for Cardiovascular Research, Charite University Hospital, and DZHK (German Centre for Cardiovascular Research), Berlin, Germany
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42
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Genetic Mechanisms Leading to Sex Differences Across Common Diseases and Anthropometric Traits. Genetics 2016; 205:979-992. [PMID: 27974502 DOI: 10.1534/genetics.116.193623] [Citation(s) in RCA: 49] [Impact Index Per Article: 5.4] [Reference Citation Analysis] [Abstract] [Key Words] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/08/2016] [Accepted: 12/08/2016] [Indexed: 01/10/2023] Open
Abstract
Common diseases often show sex differences in prevalence, onset, symptomology, treatment, or prognosis. Although studies have been performed to evaluate sex differences at specific SNP associations, this work aims to comprehensively survey a number of complex heritable diseases and anthropometric traits. Potential genetically encoded sex differences we investigated include differential genetic liability thresholds or distributions, gene-sex interaction at autosomal loci, major contribution of the X-chromosome, or gene-environment interactions reflected in genes responsive to androgens or estrogens. Finally, we tested the overlap between sex-differential association with anthropometric traits and disease risk. We utilized complementary approaches of assessing GWAS association enrichment and SNP-based heritability estimation to explore explicit sex differences, as well as enrichment in sex-implicated functional categories. We do not find consistent increased genetic load in the lower-prevalence sex, or a disproportionate role for the X-chromosome in disease risk, despite sex-heterogeneity on the X for several traits. We find that all anthropometric traits show less than complete correlation between the genetic contribution to males and females, and find a convincing example of autosome-wide genome-sex interaction in multiple sclerosis (P = 1 × 10-9). We also find some evidence for hormone-responsive gene enrichment, and striking evidence of the contribution of sex-differential anthropometric associations to common disease risk, implying that general mechanisms of sexual dimorphism determining secondary sex characteristics have shared effects on disease risk.
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43
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Autosomal and X-Linked Additive Genetic Variation for Lifespan and Aging: Comparisons Within and Between the Sexes in Drosophila melanogaster. G3-GENES GENOMES GENETICS 2016; 6:3903-3911. [PMID: 27678519 PMCID: PMC5144961 DOI: 10.1534/g3.116.028308] [Citation(s) in RCA: 9] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Indexed: 11/24/2022]
Abstract
Theory makes several predictions concerning differences in genetic variation between the X chromosome and the autosomes due to male X hemizygosity. The X chromosome should: (i) typically show relatively less standing genetic variation than the autosomes, (ii) exhibit more variation in males compared to females because of dosage compensation, and (iii) potentially be enriched with sex-specific genetic variation. Here, we address each of these predictions for lifespan and aging in Drosophila melanogaster. To achieve unbiased estimates of X and autosomal additive genetic variance, we use 80 chromosome substitution lines; 40 for the X chromosome and 40 combining the two major autosomes, which we assay for sex-specific and cross-sex genetic (co)variation. We find significant X and autosomal additive genetic variance for both traits in both sexes (with reservation for X-linked variation of aging in females), but no conclusive evidence for depletion of X-linked variation (measured through females). Males display more X-linked variation for lifespan than females, but it is unclear if this is due to dosage compensation since also autosomal variation is larger in males. Finally, our results suggest that the X chromosome is enriched for sex-specific genetic variation in lifespan but results were less conclusive for aging overall. Collectively, these results suggest that the X chromosome has reduced capacity to respond to sexually concordant selection on lifespan from standing genetic variation, while its ability to respond to sexually antagonistic selection may be augmented.
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44
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Loley C, Alver M, Assimes TL, Bjonnes A, Goel A, Gustafsson S, Hernesniemi J, Hopewell JC, Kanoni S, Kleber ME, Lau KW, Lu Y, Lyytikäinen LP, Nelson CP, Nikpay M, Qu L, Salfati E, Scholz M, Tukiainen T, Willenborg C, Won HH, Zeng L, Zhang W, Anand SS, Beutner F, Bottinger EP, Clarke R, Dedoussis G, Do R, Esko T, Eskola M, Farrall M, Gauguier D, Giedraitis V, Granger CB, Hall AS, Hamsten A, Hazen SL, Huang J, Kähönen M, Kyriakou T, Laaksonen R, Lind L, Lindgren C, Magnusson PKE, Marouli E, Mihailov E, Morris AP, Nikus K, Pedersen N, Rallidis L, Salomaa V, Shah SH, Stewart AFR, Thompson JR, Zalloua PA, Chambers JC, Collins R, Ingelsson E, Iribarren C, Karhunen PJ, Kooner JS, Lehtimäki T, Loos RJF, März W, McPherson R, Metspalu A, Reilly MP, Ripatti S, Sanghera DK, Thiery J, Watkins H, Deloukas P, Kathiresan S, Samani NJ, Schunkert H, Erdmann J, König IR. No Association of Coronary Artery Disease with X-Chromosomal Variants in Comprehensive International Meta-Analysis. Sci Rep 2016; 6:35278. [PMID: 27731410 PMCID: PMC5059659 DOI: 10.1038/srep35278] [Citation(s) in RCA: 25] [Impact Index Per Article: 2.8] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/27/2016] [Accepted: 09/26/2016] [Indexed: 11/09/2022] Open
Abstract
In recent years, genome-wide association studies have identified 58 independent risk loci for coronary artery disease (CAD) on the autosome. However, due to the sex-specific data structure of the X chromosome, it has been excluded from most of these analyses. While females have 2 copies of chromosome X, males have only one. Also, one of the female X chromosomes may be inactivated. Therefore, special test statistics and quality control procedures are required. Thus, little is known about the role of X-chromosomal variants in CAD. To fill this gap, we conducted a comprehensive X-chromosome-wide meta-analysis including more than 43,000 CAD cases and 58,000 controls from 35 international study cohorts. For quality control, sex-specific filters were used to adequately take the special structure of X-chromosomal data into account. For single study analyses, several logistic regression models were calculated allowing for inactivation of one female X-chromosome, adjusting for sex and investigating interactions between sex and genetic variants. Then, meta-analyses including all 35 studies were conducted using random effects models. None of the investigated models revealed genome-wide significant associations for any variant. Although we analyzed the largest-to-date sample, currently available methods were not able to detect any associations of X-chromosomal variants with CAD.
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Affiliation(s)
- Christina Loley
- Institut für Medizinische Biometrie und Statistik, Universität zu Lübeck, Universitätsklinikum Schleswig-Holstein, Campus Lübeck, Lübeck, Germany.,DZHK (German Centre for Cardiovascular Research), partner site Hamburg-Lübeck-Kiel, Lübeck, Germany
| | - Maris Alver
- Estonian Genome Center, University of Tartu, Tartu, Estonia.,Institute of Molecular and Cell Biology, Tartu, Estonia
| | - Themistocles L Assimes
- Department of Medicine, Division of Cardiovascular Medicine, Stanford University School of Medicine Stanford, Standford, California, USA
| | - Andrew Bjonnes
- Center for Human Genetic Research, Massachusetts General Hospital, Boston, Massachusetts, USA
| | - Anuj Goel
- Division of Cardiovascular Medicine, Radcliffe Department of Medicine, University of Oxford, Oxford, UK.,Wellcome Trust Centre for Human Genetics, University of Oxford, Oxford, UK
| | - Stefan Gustafsson
- Department of Medical Sciences, Molecular Epidemiology and Science for Life Laboratory, Uppsala University, Uppsala, Sweden
| | - Jussi Hernesniemi
- Department of Clinical Chemistry, Fimlab Laboratories, Tampere, Finland.,Department of Cardiology, Heart Hospital and University of Tampere School of Medicine, Tampere, Finland
| | - Jemma C Hopewell
- CTSU, Nuffield Department of Population Health, University of Oxford, Oxford, UK
| | - Stavroula Kanoni
- William Harvey Research Institute, Barts and The London School of Medicine and Dentistry, Queen Mary University of London, London, UK
| | - Marcus E Kleber
- Vth Department of Medicine, Medical Faculty Mannheim, Heidelberg University, Mannheim, Germany
| | - King Wai Lau
- CTSU, Nuffield Department of Population Health, University of Oxford, Oxford, UK
| | - Yingchang Lu
- The Charles Bronfman Institute for Personalized Medicine, Icahn School of Medicine at Mount Sinai, New York, USA
| | - Leo-Pekka Lyytikäinen
- Department of Clinical Chemistry, Fimlab Laboratories, Tampere, Finland.,Department of Clinical Chemistry, University of Tampere School of Medicine, Tampere, Finland
| | - Christopher P Nelson
- Department of Cardiovascular Sciences, University of Leicester, Leicester, UK.,NIHR Leicester Cardiovascular Biomedical Research Unit, Glenfield Hospital, Leicester, UK
| | - Majid Nikpay
- Ruddy Canadian Cardiovascular Genetics Centre University of Ottawa Heart Institute, Ottawa, Canada
| | - Liming Qu
- Department of Biostatistics and Epidemiology, University of Pennsylvania, Philadelphia, Pennsylvania, USA
| | - Elias Salfati
- Department of Medicine, Division of Cardiovascular Medicine, Stanford University School of Medicine Stanford, Standford, California, USA
| | - Markus Scholz
- Institute for Medical Informatics, Statistics and Epidemiology/Medical Faculty/University of Leipzig, Leipzig, Germany.,LIFE Research Center of Civilization Diseases, Leipzig, Germany
| | - Taru Tukiainen
- Analytic and Translation Genetics Unit, Massachusetts General Hospital, Boston, Massachusetts, USA.,Program in Medical and Population Genetics, Broad Institute, Cambridge, Massachusetts, USA
| | - Christina Willenborg
- DZHK (German Centre for Cardiovascular Research), partner site Hamburg-Lübeck-Kiel, Lübeck, Germany.,Institut für Integrative und Experimentelle Genomik, Universität zu Lübeck, Universitätsklinikum Schleswig-Holstein, Campus Lübeck, Lübeck, Germany and University Heart Center Luebeck, Campus Lübeck, Lübeck, Germany
| | - Hong-Hee Won
- Samsung Advanced Institute for Health Sciences and Technology (SAIHST), Sungkyunkwan University, Samsung Medical Center, Seoul, Korea
| | - Lingyao Zeng
- Deutsches Herzzentrum München, Technische Universität München, Munich, Germany.,DZHK (German Centre for Cardiovascular Research), partner site Munich Heart Alliance, München, Germany
| | - Weihua Zhang
- Department of Epidemiology and Biostatistics, Imperial College London, London, UK.,Department of Cardiology, Ealing Hospital National Health Service (NHS) Trust, Middlesex, UK
| | - Sonia S Anand
- Population Health Research Institute, McMaster University, Hamilton, Ontario, Canada
| | - Frank Beutner
- LIFE Research Center of Civilization Diseases, Leipzig, Germany.,Heart Center Leipzig, Cardiology, University of Leipzig, Leipzig, Germany
| | - Erwin P Bottinger
- The Charles Bronfman Institute for Personalized Medicine, Icahn School of Medicine at Mount Sinai, New York, USA
| | - Robert Clarke
- CTSU, Nuffield Department of Population Health, University of Oxford, Oxford, UK
| | | | - Ron Do
- The Charles Bronfman Institute for Personalized Medicine, Icahn School of Medicine at Mount Sinai, New York, USA.,The Center for Statistical Genetics, Icahn School of Medicine at Mount Sinai, New York, USA.,The Icahn Institute for Genomics and Multiscale Biology, Icahn School of Medicine at Mount Sinai, New York, USA.,The Zena and Michael A. Weiner Cardiovascular Institute, Icahn School of Medicine at Mount Sinai, New York, USA
| | - Tõnu Esko
- Estonian Genome Center, University of Tartu, Tartu, Estonia.,Department of Medicine, Harvard Medical School, Boston, Massachusetts, USA
| | - Markku Eskola
- Department of Cardiology, Heart Hospital and University of Tampere School of Medicine, Tampere, Finland
| | - Martin Farrall
- Division of Cardiovascular Medicine, Radcliffe Department of Medicine, University of Oxford, Oxford, UK.,Wellcome Trust Centre for Human Genetics, University of Oxford, Oxford, UK
| | | | - Vilmantas Giedraitis
- Department of Public Health and Caring Sciences, Geriatrics, Uppsala Universit, Uppsala, Sweden
| | | | - Alistair S Hall
- Leeds Institute of Genetics, Health and Therapeutics, University of Leeds, UK
| | - Anders Hamsten
- Cardiovascular Genetics and Genomics Group, Atherosclerosis Research Unit, Department of Medicine Solna, Karolinska Institutet, Stockholm, Sweden
| | | | - Jie Huang
- Boston VA Research Institute, Inc., Boston, Massachusetts, USA
| | - Mika Kähönen
- Department of Clinical Physiology, Tampere University Hospital, Tampere, Finland.,Department of Clinical Physiology, University of Tampere School of Medicine, Tampere, Finland
| | - Theodosios Kyriakou
- Division of Cardiovascular Medicine, Radcliffe Department of Medicine, University of Oxford, Oxford, UK.,Wellcome Trust Centre for Human Genetics, University of Oxford, Oxford, UK
| | - Reijo Laaksonen
- Department of Clinical Chemistry, Fimlab Laboratories, Tampere, Finland.,Department of Clinical Chemistry, University of Tampere School of Medicine, Tampere, Finland.,Zora Biosciences, Espoo, Finland
| | - Lars Lind
- Department of Medical Sciences, Cardiovascular Epidemiology, Uppsala University, Uppsala, Sweden
| | - Cecilia Lindgren
- Wellcome Trust Centre for Human Genetics, University of Oxford, Oxford, UK.,Broad Institute of the Massachusetts Institute of Technology and Harvard University, Cambridge, Massachusetts, USA
| | - Patrik K E Magnusson
- Department of Medical Epidemiology and Biostatistics, Karolinska Institutet, Stockholm, Sweden
| | - Eirini Marouli
- William Harvey Research Institute, Barts and The London School of Medicine and Dentistry, Queen Mary University of London, London, UK
| | | | - Andrew P Morris
- Wellcome Trust Centre for Human Genetics, University of Oxford, Oxford, UK.,Department of Biostatistics, University of Liverpool, Liverpool, UK
| | - Kjell Nikus
- Department of Cardiology, Heart Hospital and University of Tampere School of Medicine, Tampere, Finland
| | - Nancy Pedersen
- Department of Medical Epidemiology and Biostatistics, Karolinska Institutet, Stockholm, Sweden
| | - Loukianos Rallidis
- Second Department of Cardiology, University General Hospital Attikon, Athens, Greece
| | - Veikko Salomaa
- Department of Chronic Disease Prevention, National Institute for Health and Welfare, Helsinki, Finland
| | - Svati H Shah
- Duke University School of Medicine, Durham, North Carolina, USA
| | - Alexandre F R Stewart
- Ruddy Canadian Cardiovascular Genetics Centre University of Ottawa Heart Institute, Ottawa, Canada
| | - John R Thompson
- Department of Health Sciences, University of Leicester, Leicester, UK
| | - Pierre A Zalloua
- Lebanese American University, School of Medicine, Beirut, Lebanon.,Harvard School of Public Health, Boston, Massachusetts, USA
| | - John C Chambers
- Department of Cardiology, Ealing Hospital National Health Service (NHS) Trust, Middlesex, UK.,Population Health Research Institute, McMaster University, Hamilton, Ontario, Canada.,Imperial College Healthcare NHS Trust, London, UK
| | - Rory Collins
- CTSU, Nuffield Department of Population Health, University of Oxford, Oxford, UK
| | - Erik Ingelsson
- Wellcome Trust Centre for Human Genetics, University of Oxford, Oxford, UK.,Department of Medical Sciences, Molecular Epidemiology and Science for Life Laboratory, Uppsala University, Uppsala, Sweden
| | - Carlos Iribarren
- Kaiser Permanente, Division of Research, Oakland, California, USA
| | - Pekka J Karhunen
- Department of Clinical Chemistry, Fimlab Laboratories, Tampere, Finland.,Department of Forensic Medicine, University of Tampere School of Medicine, Tampere, Finland
| | - Jaspal S Kooner
- Population Health Research Institute, McMaster University, Hamilton, Ontario, Canada.,Imperial College Healthcare NHS Trust, London, UK.,Cardiovascular Science, National Heart and Lung Institute, Imperial College London, London, UK
| | - Terho Lehtimäki
- Department of Clinical Chemistry, Fimlab Laboratories, Tampere, Finland.,Department of Clinical Chemistry, University of Tampere School of Medicine, Tampere, Finland
| | - Ruth J F Loos
- The Charles Bronfman Institute for Personalized Medicine, Icahn School of Medicine at Mount Sinai, New York, USA.,The Mindich Child Health and Development Institute, Icahn School of Medicine at Mount Sinai, New York, USA
| | - Winfried März
- Vth Department of Medicine, Medical Faculty Mannheim, Heidelberg University, Mannheim, Germany.,Synlab Academy, Synlab Services GmbH, Mannheim, Germany.,Clinical Institute of Medical and Chemical Laboratory Diagnostics, Medical University of Graz, Graz, Austria
| | - Ruth McPherson
- Ruddy Canadian Cardiovascular Genetics Centre University of Ottawa Heart Institute, Ottawa, Canada
| | - Andres Metspalu
- Estonian Genome Center, University of Tartu, Tartu, Estonia.,Institute of Molecular and Cell Biology, Tartu, Estonia
| | - Muredach P Reilly
- Cardiovascular Institute, Perelman School of Medicine at the University of Pennsylvania, Philadelphia, Pennsylvania, USA
| | - Samuli Ripatti
- Kaiser Permanente, Division of Research, Oakland, California, USA.,Hjelt Institute, University of Helsinki, Helsinki, Finland.,Institute for Molecular Medicine Finland FIMM, University of Helsinki, Helsinki, Finland
| | - Dharambir K Sanghera
- Department of Pediatrics, College of Medicine, University of Oklahoma Health Sciences Center, Oklahoma City, Oklahoma, USA.,Department of Pharmaceutical Sciences, College of Pharmacy, University of Oklahoma Health Sciences Center, Oklahoma City, Oklahoma, USA.,Oklahoma Center for Neuroscience, Oklahoma City, Oklahoma, USA
| | - Joachim Thiery
- LIFE Research Center of Civilization Diseases, Leipzig, Germany.,Institute for Laboratory Medicine, Clinical Chemistry and Molecular Diagnostics, University Hospital Leipzig, Medical Faculty, Leipzig, Germany
| | - Hugh Watkins
- Division of Cardiovascular Medicine, Radcliffe Department of Medicine, University of Oxford, Oxford, UK.,Wellcome Trust Centre for Human Genetics, University of Oxford, Oxford, UK
| | - Panos Deloukas
- William Harvey Research Institute, Barts and The London School of Medicine and Dentistry, Queen Mary University of London, London, UK.,Wellcome Trust Sanger Institute, Hinxton, Cambridge, UK.,Princess Al-Jawhara Al-Brahim Centre of Excellence in Research of Hereditary Disorders (PACER-HD), King Abdulaziz University, Jeddah, Saudi Arabia
| | - Sekar Kathiresan
- Center for Human Genetic Research, Massachusetts General Hospital, Boston, Massachusetts, USA.,Program in Medical and Population Genetics, Broad Institute, Cambridge, Massachusetts, USA.,Department of Medicine, Harvard Medical School, Boston, Massachusetts, USA.,Cardiovascular Research Center, Massachusetts General Hospital, Boston, Massachusetts, USA
| | - Nilesh J Samani
- Department of Cardiovascular Sciences, University of Leicester, Leicester, UK.,NIHR Leicester Cardiovascular Biomedical Research Unit, Glenfield Hospital, Leicester, UK
| | - Heribert Schunkert
- DZHK (German Centre for Cardiovascular Research), partner site Munich Heart Alliance, München, Germany.,Department of Epidemiology and Biostatistics, Imperial College London, London, UK
| | - Jeanette Erdmann
- DZHK (German Centre for Cardiovascular Research), partner site Hamburg-Lübeck-Kiel, Lübeck, Germany.,Institut für Integrative und Experimentelle Genomik, Universität zu Lübeck, Universitätsklinikum Schleswig-Holstein, Campus Lübeck, Lübeck, Germany and University Heart Center Luebeck, Campus Lübeck, Lübeck, Germany
| | - Inke R König
- Institut für Medizinische Biometrie und Statistik, Universität zu Lübeck, Universitätsklinikum Schleswig-Holstein, Campus Lübeck, Lübeck, Germany.,DZHK (German Centre for Cardiovascular Research), partner site Hamburg-Lübeck-Kiel, Lübeck, Germany
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45
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Lu C, Wen Y, Hu W, Lu F, Qin Y, Wang Y, Li S, Yang S, Lin Y, Wang C, Jin L, Shen H, Sha J, Wang X, Hu Z, Xia Y. Y chromosome haplogroups based genome-wide association study pinpoints revelation for interactions on non-obstructive azoospermia. Sci Rep 2016; 6:33363. [PMID: 27628680 PMCID: PMC5024297 DOI: 10.1038/srep33363] [Citation(s) in RCA: 7] [Impact Index Per Article: 0.8] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/10/2016] [Accepted: 08/25/2016] [Indexed: 01/02/2023] Open
Abstract
The Y chromosome has high genetic variability with low rates of parallel and back mutations, which make up the most informative haplotyping system. To examine whether Y chromosome haplogroups (Y-hgs) could modify the effects of autosomal variants on non-obstructive azoospermia (NOA), based on our previous genome-wide association study (GWAS), we conducted a genetic interaction analysis in GWAS subjects. Logistic regression analysis demonstrated a protective effect of Y-hg O3e(*) on NOA. Then, we explored the potential interaction between Y-hg O3e(*) and autosomal variants. Our results demonstrated that there was a suggestively significant interaction between Y-hg O3e(*) and rs11135484 on NOA (Pinter = 9.89 × 10(-5)). Bioinformatic analysis revealed that genes annotated by significant single nucleotide polymorphisms (SNPs) were mainly enriched in immunological pathways. This is the first study of interactions between Y-hgs and autosomal variants on a genome-wide scale, which addresses the missing heritability in spermatogenic impairment and sheds new light on the pathogenesis of male infertility.
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Affiliation(s)
- Chuncheng Lu
- State Key Laboratory of Reproductive Medicine, Institute of Toxicology, Nanjing Medical University, Nanjing 210029, China
- Key Laboratory of Modern Toxicology of Ministry of Education, School of Public Health, Nanjing Medical University, Nanjing 210029, China
| | - Yang Wen
- Department of Epidemiology and Biostatistics and Key Laboratory of Modern Toxicology of Ministry of Education, School of Public Health, Nanjing Medical University, Nanjing, China
| | - Weiyue Hu
- Key Laboratory of Modern Toxicology of Ministry of Education, School of Public Health, Nanjing Medical University, Nanjing 210029, China
| | - Feng Lu
- Department of Epidemiology and Biostatistics and Key Laboratory of Modern Toxicology of Ministry of Education, School of Public Health, Nanjing Medical University, Nanjing, China
| | - Yufeng Qin
- Key Laboratory of Modern Toxicology of Ministry of Education, School of Public Health, Nanjing Medical University, Nanjing 210029, China
| | - Ying Wang
- Key Laboratory of Modern Toxicology of Ministry of Education, School of Public Health, Nanjing Medical University, Nanjing 210029, China
| | - Shilin Li
- State Key Laboratory of Genetic Engineering and Ministry of Education Key Laboratory of Contemporary Anthropology, School of Life Sciences, Fudan University, Shanghai 200433, China
| | - Shuping Yang
- State Key Laboratory of Genetic Engineering and Ministry of Education Key Laboratory of Contemporary Anthropology, School of Life Sciences, Fudan University, Shanghai 200433, China
| | - Yuan Lin
- Department of Epidemiology and Biostatistics and Key Laboratory of Modern Toxicology of Ministry of Education, School of Public Health, Nanjing Medical University, Nanjing, China
| | - Cheng Wang
- Department of Epidemiology and Biostatistics and Key Laboratory of Modern Toxicology of Ministry of Education, School of Public Health, Nanjing Medical University, Nanjing, China
| | - Li Jin
- State Key Laboratory of Genetic Engineering and Ministry of Education Key Laboratory of Contemporary Anthropology, School of Life Sciences, Fudan University, Shanghai 200433, China
- Institutes of Biomedical Sciences, Fudan University, Shanghai 200032, China
| | - Hongbing Shen
- State Key Laboratory of Reproductive Medicine, Institute of Toxicology, Nanjing Medical University, Nanjing 210029, China
- Department of Epidemiology and Biostatistics and Key Laboratory of Modern Toxicology of Ministry of Education, School of Public Health, Nanjing Medical University, Nanjing, China
| | - Jiahao Sha
- State Key Laboratory of Reproductive Medicine, Institute of Toxicology, Nanjing Medical University, Nanjing 210029, China
| | - Xinru Wang
- State Key Laboratory of Reproductive Medicine, Institute of Toxicology, Nanjing Medical University, Nanjing 210029, China
- Key Laboratory of Modern Toxicology of Ministry of Education, School of Public Health, Nanjing Medical University, Nanjing 210029, China
| | - Zhibin Hu
- State Key Laboratory of Reproductive Medicine, Institute of Toxicology, Nanjing Medical University, Nanjing 210029, China
- Department of Epidemiology and Biostatistics and Key Laboratory of Modern Toxicology of Ministry of Education, School of Public Health, Nanjing Medical University, Nanjing, China
| | - Yankai Xia
- State Key Laboratory of Reproductive Medicine, Institute of Toxicology, Nanjing Medical University, Nanjing 210029, China
- Key Laboratory of Modern Toxicology of Ministry of Education, School of Public Health, Nanjing Medical University, Nanjing 210029, China
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46
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Kukurba KR, Parsana P, Balliu B, Smith KS, Zappala Z, Knowles DA, Favé MJ, Davis JR, Li X, Zhu X, Potash JB, Weissman MM, Shi J, Kundaje A, Levinson DF, Awadalla P, Mostafavi S, Battle A, Montgomery SB. Impact of the X Chromosome and sex on regulatory variation. Genome Res 2016; 26:768-77. [PMID: 27197214 PMCID: PMC4889977 DOI: 10.1101/gr.197897.115] [Citation(s) in RCA: 73] [Impact Index Per Article: 8.1] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/06/2015] [Accepted: 04/18/2016] [Indexed: 02/07/2023]
Abstract
The X Chromosome, with its unique mode of inheritance, contributes to differences between the sexes at a molecular level, including sex-specific gene expression and sex-specific impact of genetic variation. Improving our understanding of these differences offers to elucidate the molecular mechanisms underlying sex-specific traits and diseases. However, to date, most studies have either ignored the X Chromosome or had insufficient power to test for the sex-specific impact of genetic variation. By analyzing whole blood transcriptomes of 922 individuals, we have conducted the first large-scale, genome-wide analysis of the impact of both sex and genetic variation on patterns of gene expression, including comparison between the X Chromosome and autosomes. We identified a depletion of expression quantitative trait loci (eQTL) on the X Chromosome, especially among genes under high selective constraint. In contrast, we discovered an enrichment of sex-specific regulatory variants on the X Chromosome. To resolve the molecular mechanisms underlying such effects, we generated chromatin accessibility data through ATAC-sequencing to connect sex-specific chromatin accessibility to sex-specific patterns of expression and regulatory variation. As sex-specific regulatory variants discovered in our study can inform sex differences in heritable disease prevalence, we integrated our data with genome-wide association study data for multiple immune traits identifying several traits with significant sex biases in genetic susceptibilities. Together, our study provides genome-wide insight into how genetic variation, the X Chromosome, and sex shape human gene regulation and disease.
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Affiliation(s)
- Kimberly R Kukurba
- Department of Pathology, Stanford University School of Medicine, Stanford, California 94305, USA; Department of Genetics, Stanford University School of Medicine, Stanford, California 94305, USA
| | - Princy Parsana
- Department of Computer Science, Johns Hopkins University, Baltimore, Maryland 21218, USA
| | - Brunilda Balliu
- Department of Pathology, Stanford University School of Medicine, Stanford, California 94305, USA
| | - Kevin S Smith
- Department of Pathology, Stanford University School of Medicine, Stanford, California 94305, USA
| | - Zachary Zappala
- Department of Pathology, Stanford University School of Medicine, Stanford, California 94305, USA; Department of Genetics, Stanford University School of Medicine, Stanford, California 94305, USA
| | - David A Knowles
- Department of Computer Science, Johns Hopkins University, Baltimore, Maryland 21218, USA
| | - Marie-Julie Favé
- Sainte-Justine University Hospital Research Centre, Department of Pediatrics, University of Montreal, Montreal, Québec H3T 1J4, Canada
| | - Joe R Davis
- Department of Pathology, Stanford University School of Medicine, Stanford, California 94305, USA; Department of Genetics, Stanford University School of Medicine, Stanford, California 94305, USA
| | - Xin Li
- Department of Pathology, Stanford University School of Medicine, Stanford, California 94305, USA
| | - Xiaowei Zhu
- Department of Psychiatry, Stanford University School of Medicine, Stanford, California 94305, USA
| | - James B Potash
- Department of Psychiatry, University of Iowa Hospitals & Clinics, Iowa City, Iowa 52242, USA
| | - Myrna M Weissman
- Department of Psychiatry, Columbia University and New York State Psychiatric Institute, New York, New York 10032, USA
| | - Jianxin Shi
- Division of Cancer Epidemiology and Genetics, National Cancer Institute, Bethesda, Maryland 20892, USA
| | - Anshul Kundaje
- Department of Genetics, Stanford University School of Medicine, Stanford, California 94305, USA; Department of Computer Science, Stanford University, Stanford, California 94305, USA
| | - Douglas F Levinson
- Department of Psychiatry, Stanford University School of Medicine, Stanford, California 94305, USA
| | - Philip Awadalla
- Sainte-Justine University Hospital Research Centre, Department of Pediatrics, University of Montreal, Montreal, Québec H3T 1J4, Canada
| | - Sara Mostafavi
- Department of Statistics, University of British Columbia, Vancouver, British Columbia V6T 1Z4, Canada
| | - Alexis Battle
- Department of Computer Science, Johns Hopkins University, Baltimore, Maryland 21218, USA;
| | - Stephen B Montgomery
- Department of Pathology, Stanford University School of Medicine, Stanford, California 94305, USA; Department of Genetics, Stanford University School of Medicine, Stanford, California 94305, USA; Department of Computer Science, Stanford University, Stanford, California 94305, USA;
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47
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Fiot E, Zenaty D, Boizeau P, Haigneré J, Dos Santos S, Léger J. X-chromosome gene dosage as a determinant of impaired pre and postnatal growth and adult height in Turner syndrome. Eur J Endocrinol 2016; 174:281-8. [PMID: 26744895 DOI: 10.1530/eje-15-1000] [Citation(s) in RCA: 16] [Impact Index Per Article: 1.8] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 10/09/2015] [Accepted: 12/14/2015] [Indexed: 12/28/2022]
Abstract
OBJECTIVE Short stature is a key aspect of the phenotype of patients with Turner syndrome (TS). SHOX haploinsufficiency is responsible for about two-thirds of the height deficit. The aim was to investigate the effect of X-chromosome gene dosage on anthropometric parameters at birth, spontaneous height, and adult height (AH) after growth hormone (GH) treatment. DESIGN We conducted a national observational multicenter study. METHODS Birth parameter SDS for gestational age, height, and AH before and after GH treatment respectively, and height deficit with respect to target height (SDS) were classified by karyotype subgroup in a cohort of 1501 patients with TS: 45,X (36%), isoXq (19%), 45,X/46,XX (15%), XrX (7%), presence of Y (6%), or other karyotypes (17%). RESULTS Birth weight, length (P<0.0001), and head circumference (P<0.001), height and height deficit with respect to target height (SDS) before GH treatment, at a median age of 8.8 (5.3-11.8) years and after adjustment for age and correction for multiple testing (P<0.0001), and AH deficit with respect to target height at a median age of 19.3 (18.0-21.8) years and with additional adjustment for dose and duration of GH treatment (P=0.006), were significantly associated with karyotype subgroup. Growth retardation tended to be more severe in patients with XrX, isoXq, and, to a lesser extent, 45,X karyotypes than in patients with 45,X/46,XX karyotypes or a Y chromosome. CONCLUSION These data suggest that haploinsufficiency for an unknown Xp gene increases the risk of fetal and postnatal growth deficit and short AH with respect to target height after GH therapy.
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Affiliation(s)
- Elodie Fiot
- Assistance Publique-Hôpitaux de ParisHôpital Robert Debré, Service d'Endocrinologie Diabétologie Pédiatrique, Centre de Référence des Maladies Endocriniennes Rares de la Croissance, INSERM U 1141, 48 Bd Sérurier, F-75019 Paris, FranceUniversité Paris DiderotSorbonne Paris Cité, F-75019 Paris, FranceInstitut National de la Santé et de la Recherche Médicale (Inserm)Unité 1141, DHU Protect, F-75019 Paris, FranceAP-HPHôpital Robert Debré, Unit of Clinical Epidemiology, F-75019, Paris, FranceInsermCIC-EC 1426, F-75019 Paris, France
| | - Delphine Zenaty
- Assistance Publique-Hôpitaux de ParisHôpital Robert Debré, Service d'Endocrinologie Diabétologie Pédiatrique, Centre de Référence des Maladies Endocriniennes Rares de la Croissance, INSERM U 1141, 48 Bd Sérurier, F-75019 Paris, FranceUniversité Paris DiderotSorbonne Paris Cité, F-75019 Paris, FranceInstitut National de la Santé et de la Recherche Médicale (Inserm)Unité 1141, DHU Protect, F-75019 Paris, FranceAP-HPHôpital Robert Debré, Unit of Clinical Epidemiology, F-75019, Paris, FranceInsermCIC-EC 1426, F-75019 Paris, France
| | - Priscilla Boizeau
- Assistance Publique-Hôpitaux de ParisHôpital Robert Debré, Service d'Endocrinologie Diabétologie Pédiatrique, Centre de Référence des Maladies Endocriniennes Rares de la Croissance, INSERM U 1141, 48 Bd Sérurier, F-75019 Paris, FranceUniversité Paris DiderotSorbonne Paris Cité, F-75019 Paris, FranceInstitut National de la Santé et de la Recherche Médicale (Inserm)Unité 1141, DHU Protect, F-75019 Paris, FranceAP-HPHôpital Robert Debré, Unit of Clinical Epidemiology, F-75019, Paris, FranceInsermCIC-EC 1426, F-75019 Paris, France Assistance Publique-Hôpitaux de ParisHôpital Robert Debré, Service d'Endocrinologie Diabétologie Pédiatrique, Centre de Référence des Maladies Endocriniennes Rares de la Croissance, INSERM U 1141, 48 Bd Sérurier, F-75019 Paris, FranceUniversité Paris DiderotSorbonne Paris Cité, F-75019 Paris, FranceInstitut National de la Santé et de la Recherche Médicale (Inserm)Unité 1141, DHU Protect, F-75019 Paris, FranceAP-HPHôpital Robert Debré, Unit of Clinical Epidemiology, F-75019, Paris, FranceInsermCIC-EC 1426, F-75019 Paris, France
| | - Jeremy Haigneré
- Assistance Publique-Hôpitaux de ParisHôpital Robert Debré, Service d'Endocrinologie Diabétologie Pédiatrique, Centre de Référence des Maladies Endocriniennes Rares de la Croissance, INSERM U 1141, 48 Bd Sérurier, F-75019 Paris, FranceUniversité Paris DiderotSorbonne Paris Cité, F-75019 Paris, FranceInstitut National de la Santé et de la Recherche Médicale (Inserm)Unité 1141, DHU Protect, F-75019 Paris, FranceAP-HPHôpital Robert Debré, Unit of Clinical Epidemiology, F-75019, Paris, FranceInsermCIC-EC 1426, F-75019 Paris, France Assistance Publique-Hôpitaux de ParisHôpital Robert Debré, Service d'Endocrinologie Diabétologie Pédiatrique, Centre de Référence des Maladies Endocriniennes Rares de la Croissance, INSERM U 1141, 48 Bd Sérurier, F-75019 Paris, FranceUniversité Paris DiderotSorbonne Paris Cité, F-75019 Paris, FranceInstitut National de la Santé et de la Recherche Médicale (Inserm)Unité 1141, DHU Protect, F-75019 Paris, FranceAP-HPHôpital Robert Debré, Unit of Clinical Epidemiology, F-75019, Paris, FranceInsermCIC-EC 1426, F-75019 Paris, France
| | - Sophie Dos Santos
- Assistance Publique-Hôpitaux de ParisHôpital Robert Debré, Service d'Endocrinologie Diabétologie Pédiatrique, Centre de Référence des Maladies Endocriniennes Rares de la Croissance, INSERM U 1141, 48 Bd Sérurier, F-75019 Paris, FranceUniversité Paris DiderotSorbonne Paris Cité, F-75019 Paris, FranceInstitut National de la Santé et de la Recherche Médicale (Inserm)Unité 1141, DHU Protect, F-75019 Paris, FranceAP-HPHôpital Robert Debré, Unit of Clinical Epidemiology, F-75019, Paris, FranceInsermCIC-EC 1426, F-75019 Paris, France
| | - Juliane Léger
- Assistance Publique-Hôpitaux de ParisHôpital Robert Debré, Service d'Endocrinologie Diabétologie Pédiatrique, Centre de Référence des Maladies Endocriniennes Rares de la Croissance, INSERM U 1141, 48 Bd Sérurier, F-75019 Paris, FranceUniversité Paris DiderotSorbonne Paris Cité, F-75019 Paris, FranceInstitut National de la Santé et de la Recherche Médicale (Inserm)Unité 1141, DHU Protect, F-75019 Paris, FranceAP-HPHôpital Robert Debré, Unit of Clinical Epidemiology, F-75019, Paris, FranceInsermCIC-EC 1426, F-75019 Paris, France Assistance Publique-Hôpitaux de ParisHôpital Robert Debré, Service d'Endocrinologie Diabétologie Pédiatrique, Centre de Référence des Maladies Endocriniennes Rares de la Croissance, INSERM U 1141, 48 Bd Sérurier, F-75019 Paris, FranceUniversité Paris DiderotSorbonne Paris Cité, F-75019 Paris, FranceInstitut National de la Santé et de la Recherche Médicale (Inserm)Unité 1141, DHU Protect, F-75019 Paris, FranceAP-HPHôpital Robert Debré, Unit of Clinical Epidemiology, F-75019, Paris, FranceInsermCIC-EC 1426, F-75019 Paris, France Assistance Publique-Hôpitaux de ParisHôpital Robert Debré, Service d'Endocrinologie Diabétologie Pédiatrique, Centre de Référence des Maladies Endocriniennes Rares de la Croissance, INSERM U 1141, 48 Bd Sérurier, F-75019 Paris, FranceUniversité Paris DiderotSorbonne Paris Cité, F-75019 Paris, FranceInstitut National de la Santé et de la Recherche Médicale (Inserm)Unité 1141, DHU Protect, F-75019 Paris, FranceAP-HPHôpital Robert Debré, Unit of Clinical Epidemiology, F-75019, Paris, FranceInsermCIC-EC 1426, F-75019 Paris, France
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48
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Slavney A, Arbiza L, Clark AG, Keinan A. Strong Constraint on Human Genes Escaping X-Inactivation Is Modulated by their Expression Level and Breadth in Both Sexes. Mol Biol Evol 2015; 33:384-93. [PMID: 26494842 PMCID: PMC4751236 DOI: 10.1093/molbev/msv225] [Citation(s) in RCA: 15] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/11/2022] Open
Abstract
In eutherian mammals, X-linked gene expression is normalized between XX females and XY males through the process of X chromosome inactivation (XCI). XCI results in silencing of transcription from one ChrX homolog per female cell. However, approximately 25% of human ChrX genes escape XCI to some extent and exhibit biallelic expression in females. The evolutionary basis of this phenomenon is not entirely clear, but high sequence conservation of XCI escapers suggests that purifying selection may directly or indirectly drive XCI escape at these loci. One hypothesis is that this signal results from contributions to developmental and physiological sex differences, but presently there is limited evidence supporting this model in humans. Another potential driver of this signal is selection for high and/or broad gene expression in both sexes, which are strong predictors of reduced nucleotide substitution rates in mammalian genes. Here, we compared purifying selection and gene expression patterns of human XCI escapers with those of X-inactivated genes in both sexes. When we accounted for the functional status of each ChrX gene’s Y-linked homolog (or “gametolog”), we observed that XCI escapers exhibit greater degrees of purifying selection in the human lineage than X-inactivated genes, as well as higher and broader gene expression than X-inactivated genes across tissues in both sexes. These results highlight a significant role for gene expression in both sexes in driving purifying selection on XCI escapers, and emphasize these genes’ potential importance in human disease.
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Affiliation(s)
- Andrea Slavney
- Department of Biological Statistics and Computational Biology, Cornell University Department of Molecular Biology and Genetics, Cornell University
| | - Leonardo Arbiza
- Department of Biological Statistics and Computational Biology, Cornell University
| | - Andrew G Clark
- Department of Biological Statistics and Computational Biology, Cornell University Department of Molecular Biology and Genetics, Cornell University
| | - Alon Keinan
- Department of Biological Statistics and Computational Biology, Cornell University
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49
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Gao F, Chang D, Biddanda A, Ma L, Guo Y, Zhou Z, Keinan A. XWAS: A Software Toolset for Genetic Data Analysis and Association Studies of the X Chromosome. J Hered 2015; 106:666-71. [PMID: 26268243 PMCID: PMC4567842 DOI: 10.1093/jhered/esv059] [Citation(s) in RCA: 88] [Impact Index Per Article: 8.8] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/07/2015] [Accepted: 07/20/2015] [Indexed: 12/21/2022] Open
Abstract
XWAS is a new software suite for the analysis of the X chromosome in association studies and similar genetic studies. The X chromosome plays an important role in human disease and traits of many species, especially those with sexually dimorphic characteristics. Special attention needs to be given to its analysis due to the unique inheritance pattern, which leads to analytical complications that have resulted in the majority of genome-wide association studies (GWAS) either not considering X or mishandling it with toolsets that had been designed for non-sex chromosomes. We hence developed XWAS to fill the need for tools that are specially designed for analysis of X. Following extensive, stringent, and X-specific quality control, XWAS offers an array of statistical tests of association, including: 1) the standard test between a SNP (single nucleotide polymorphism) and disease risk, including after first stratifying individuals by sex, 2) a test for a differential effect of a SNP on disease between males and females, 3) motivated by X-inactivation, a test for higher variance of a trait in heterozygous females as compared with homozygous females, and 4) for all tests, a version that allows for combining evidence from all SNPs across a gene. We applied the toolset analysis pipeline to 16 GWAS datasets of immune-related disorders and 7 risk factors of coronary artery disease, and discovered several new X-linked genetic associations. XWAS will provide the tools and incentive for others to incorporate the X chromosome into GWAS and similar studies in any species with an XX/XY system, hence enabling discoveries of novel loci implicated in many diseases and in their sexual dimorphism.
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Affiliation(s)
- Feng Gao
- From the Department of Biological Statistics and Computational Biology, Cornell University, Ithaca, NY 14853 (Gao, Chang, Biddanda, Ma, Guo, Zhou, and Keinan); Program in Computational Biology and Medicine, Cornell University, Ithaca, NY 14853 (Chang and Keinan); Department of Animal and Avian Sciences, University of Maryland, College Park, MD 20740 (Ma); and School of Computer Science and Technology, Harbin Institute of Technology, Harbin, Heilongjiang 150001, China (Guo)
| | - Diana Chang
- From the Department of Biological Statistics and Computational Biology, Cornell University, Ithaca, NY 14853 (Gao, Chang, Biddanda, Ma, Guo, Zhou, and Keinan); Program in Computational Biology and Medicine, Cornell University, Ithaca, NY 14853 (Chang and Keinan); Department of Animal and Avian Sciences, University of Maryland, College Park, MD 20740 (Ma); and School of Computer Science and Technology, Harbin Institute of Technology, Harbin, Heilongjiang 150001, China (Guo)
| | - Arjun Biddanda
- From the Department of Biological Statistics and Computational Biology, Cornell University, Ithaca, NY 14853 (Gao, Chang, Biddanda, Ma, Guo, Zhou, and Keinan); Program in Computational Biology and Medicine, Cornell University, Ithaca, NY 14853 (Chang and Keinan); Department of Animal and Avian Sciences, University of Maryland, College Park, MD 20740 (Ma); and School of Computer Science and Technology, Harbin Institute of Technology, Harbin, Heilongjiang 150001, China (Guo)
| | - Li Ma
- From the Department of Biological Statistics and Computational Biology, Cornell University, Ithaca, NY 14853 (Gao, Chang, Biddanda, Ma, Guo, Zhou, and Keinan); Program in Computational Biology and Medicine, Cornell University, Ithaca, NY 14853 (Chang and Keinan); Department of Animal and Avian Sciences, University of Maryland, College Park, MD 20740 (Ma); and School of Computer Science and Technology, Harbin Institute of Technology, Harbin, Heilongjiang 150001, China (Guo)
| | - Yingjie Guo
- From the Department of Biological Statistics and Computational Biology, Cornell University, Ithaca, NY 14853 (Gao, Chang, Biddanda, Ma, Guo, Zhou, and Keinan); Program in Computational Biology and Medicine, Cornell University, Ithaca, NY 14853 (Chang and Keinan); Department of Animal and Avian Sciences, University of Maryland, College Park, MD 20740 (Ma); and School of Computer Science and Technology, Harbin Institute of Technology, Harbin, Heilongjiang 150001, China (Guo)
| | - Zilu Zhou
- From the Department of Biological Statistics and Computational Biology, Cornell University, Ithaca, NY 14853 (Gao, Chang, Biddanda, Ma, Guo, Zhou, and Keinan); Program in Computational Biology and Medicine, Cornell University, Ithaca, NY 14853 (Chang and Keinan); Department of Animal and Avian Sciences, University of Maryland, College Park, MD 20740 (Ma); and School of Computer Science and Technology, Harbin Institute of Technology, Harbin, Heilongjiang 150001, China (Guo)
| | - Alon Keinan
- From the Department of Biological Statistics and Computational Biology, Cornell University, Ithaca, NY 14853 (Gao, Chang, Biddanda, Ma, Guo, Zhou, and Keinan); Program in Computational Biology and Medicine, Cornell University, Ithaca, NY 14853 (Chang and Keinan); Department of Animal and Avian Sciences, University of Maryland, College Park, MD 20740 (Ma); and School of Computer Science and Technology, Harbin Institute of Technology, Harbin, Heilongjiang 150001, China (Guo).
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Ma L, Hoffman G, Keinan A. X-inactivation informs variance-based testing for X-linked association of a quantitative trait. BMC Genomics 2015; 16:241. [PMID: 25880738 PMCID: PMC4381508 DOI: 10.1186/s12864-015-1463-y] [Citation(s) in RCA: 20] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/03/2014] [Accepted: 03/13/2015] [Indexed: 01/06/2023] Open
Abstract
Background The X chromosome plays an important role in human diseases and traits. However, few X-linked associations have been reported in genome-wide association studies, partly due to analytical complications and low statistical power. Results In this study, we propose tests of X-linked association that capitalize on variance heterogeneity caused by various factors, predominantly the process of X-inactivation. In the presence of X-inactivation, the expression of one copy of the chromosome is randomly silenced. Due to the consequent elevated randomness of expressed variants, females that are heterozygotes for a quantitative trait locus might exhibit higher phenotypic variance for that trait. We propose three tests that build on this phenomenon: 1) A test for inflated variance in heterozygous females; 2) A weighted association test; and 3) A combined test. Test 1 captures the novel signal proposed herein by directly testing for higher phenotypic variance of heterozygous than homozygous females. As a test of variance it is generally less powerful than standard tests of association that consider means, which is supported by extensive simulations. Test 2 is similar to a standard association test in considering the phenotypic mean, but differs by accounting for (rather than testing) the variance heterogeneity. As expected in light of X-inactivation, this test is slightly more powerful than a standard association test. Finally, test 3 further improves power by combining the results of the first two tests. We applied the these tests to the ARIC cohort data and identified a novel X-linked association near gene AFF2 with blood pressure, which was not significant based on standard association testing of mean blood pressure. Conclusions Variance-based tests examine overdispersion, thereby providing a complementary type of signal to a standard association test. Our results point to the potential to improve power of detecting X-linked associations in the presence of variance heterogeneity.
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Affiliation(s)
- Li Ma
- Department of Animal and Avian Sciences, University of Maryland, College Park, MD, 20740, USA. .,Department of Biological Statistics and Computational Biology, Cornell University, Ithaca, NY, 14850, USA.
| | - Gabriel Hoffman
- Department of Biological Statistics and Computational Biology, Cornell University, Ithaca, NY, 14850, USA. .,Present address: Icahn Institute for Genomics and Multiscale Biology, Department of Genetics and Genomic Sciences, Icahn School of Medicine at Mount Sinai, New York, USA.
| | - Alon Keinan
- Department of Biological Statistics and Computational Biology, Cornell University, Ithaca, NY, 14850, USA.
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