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Bellavance J, Wang L, Gagliano Taliun SA. Eight quick tips for including chromosome X in genome-wide association studies. PLoS Comput Biol 2024; 20:e1012160. [PMID: 38843110 PMCID: PMC11156303 DOI: 10.1371/journal.pcbi.1012160] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [MESH Headings] [Grants] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 06/09/2024] Open
Affiliation(s)
- Justin Bellavance
- Faculty of Medicine, Université de Montréal, Montréal, Québec, Canada
- Research Centre, Montréal Heart Institute, Montréal, Québec, Canada
| | - Linda Wang
- Faculty of Medicine, Université de Montréal, Montréal, Québec, Canada
- Research Centre, Montréal Heart Institute, Montréal, Québec, Canada
| | - Sarah A. Gagliano Taliun
- Research Centre, Montréal Heart Institute, Montréal, Québec, Canada
- Department of Medicine, Faculty of Medicine, Université de Montréal, Montréal, Québec, Canada
- Department of Neurosciences, Faculty of Medicine, Université de Montréal, Montréal, Québec, Canada
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Wang KW, Yuan YX, Zhu B, Zhang Y, Wei YF, Meng FS, Zhang S, Wang JX, Zhou JY. X chromosome-wide association study of quantitative biomarkers from the Alzheimer's Disease Neuroimaging Initiative study. Front Aging Neurosci 2023; 15:1277731. [PMID: 38035272 PMCID: PMC10682795 DOI: 10.3389/fnagi.2023.1277731] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/15/2023] [Accepted: 10/20/2023] [Indexed: 12/02/2023] Open
Abstract
Introduction Alzheimer's disease (AD) is a complex neurodegenerative disease with high heritability. Compared to autosomes, a higher proportion of disorder-associated genes on X chromosome are expressed in the brain. However, only a few studies focused on the identification of the susceptibility loci for AD on X chromosome. Methods Using the data from the Alzheimer's Disease Neuroimaging Initiative Study, we conducted an X chromosome-wide association study between 16 AD quantitative biomarkers and 19,692 single nucleotide polymorphisms (SNPs) based on both the cross-sectional and longitudinal studies. Results We identified 15 SNPs statistically significantly associated with different quantitative biomarkers of the AD. For the cross-sectional study, six SNPs (rs5927116, rs4596772, rs5929538, rs2213488, rs5920524, and rs5945306) are located in or near to six genes DMD, TBX22, LOC101928437, TENM1, SPANXN1, and ZFP92, which have been reported to be associated with schizophrenia or neuropsychiatric diseases in literature. For the longitudinal study, four SNPs (rs4829868, rs5931111, rs6540385, and rs763320) are included in or near to two genes RAC1P4 and AFF2, which have been demonstrated to be associated with brain development or intellectual disability in literature, while the functional annotations of other five novel SNPs (rs12157031, rs428303, rs5953487, rs10284107, and rs5955016) have not been found. Discussion 15 SNPs were found statistically significantly associated with the quantitative biomarkers of the AD. Follow-up study in molecular genetics is needed to verify whether they are indeed related to AD. The findings in this article expand our understanding of the role of the X chromosome in exploring disease susceptibility, introduce new insights into the molecular genetics behind the AD, and may provide a mechanistic clue to further AD-related studies.
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Affiliation(s)
- Kai-Wen Wang
- State Key Laboratory of Organ Failure Research, Ministry of Education, Guangdong Provincial Key Laboratory of Tropical Disease Research, Department of Biostatistics, School of Public Health, Southern Medical University, Guangzhou, China
- Guangdong-Hong Kong-Macao Joint Laboratory for Contaminants Exposure and Health, Guangzhou, China
| | - Yu-Xin Yuan
- State Key Laboratory of Organ Failure Research, Ministry of Education, Guangdong Provincial Key Laboratory of Tropical Disease Research, Department of Biostatistics, School of Public Health, Southern Medical University, Guangzhou, China
- Guangdong-Hong Kong-Macao Joint Laboratory for Contaminants Exposure and Health, Guangzhou, China
| | - Bin Zhu
- State Key Laboratory of Organ Failure Research, Ministry of Education, Guangdong Provincial Key Laboratory of Tropical Disease Research, Department of Biostatistics, School of Public Health, Southern Medical University, Guangzhou, China
- Guangdong-Hong Kong-Macao Joint Laboratory for Contaminants Exposure and Health, Guangzhou, China
| | - Yi Zhang
- State Key Laboratory of Organ Failure Research, Ministry of Education, Guangdong Provincial Key Laboratory of Tropical Disease Research, Department of Biostatistics, School of Public Health, Southern Medical University, Guangzhou, China
- Guangdong-Hong Kong-Macao Joint Laboratory for Contaminants Exposure and Health, Guangzhou, China
| | - Yi-Fang Wei
- State Key Laboratory of Organ Failure Research, Ministry of Education, Guangdong Provincial Key Laboratory of Tropical Disease Research, Department of Biostatistics, School of Public Health, Southern Medical University, Guangzhou, China
- Guangdong-Hong Kong-Macao Joint Laboratory for Contaminants Exposure and Health, Guangzhou, China
| | - Fan-Shuo Meng
- State Key Laboratory of Organ Failure Research, Ministry of Education, Guangdong Provincial Key Laboratory of Tropical Disease Research, Department of Biostatistics, School of Public Health, Southern Medical University, Guangzhou, China
- Guangdong-Hong Kong-Macao Joint Laboratory for Contaminants Exposure and Health, Guangzhou, China
| | - Shun Zhang
- State Key Laboratory of Organ Failure Research, Ministry of Education, Guangdong Provincial Key Laboratory of Tropical Disease Research, Department of Biostatistics, School of Public Health, Southern Medical University, Guangzhou, China
| | - Jing-Xuan Wang
- State Key Laboratory of Organ Failure Research, Ministry of Education, Guangdong Provincial Key Laboratory of Tropical Disease Research, Department of Biostatistics, School of Public Health, Southern Medical University, Guangzhou, China
| | - Ji-Yuan Zhou
- State Key Laboratory of Organ Failure Research, Ministry of Education, Guangdong Provincial Key Laboratory of Tropical Disease Research, Department of Biostatistics, School of Public Health, Southern Medical University, Guangzhou, China
- Guangdong-Hong Kong-Macao Joint Laboratory for Contaminants Exposure and Health, Guangzhou, China
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Wiese CB, Avetisyan R, Reue K. The impact of chromosomal sex on cardiometabolic health and disease. Trends Endocrinol Metab 2023; 34:652-665. [PMID: 37598068 PMCID: PMC11090013 DOI: 10.1016/j.tem.2023.07.003] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 03/22/2023] [Revised: 07/14/2023] [Accepted: 07/20/2023] [Indexed: 08/21/2023]
Abstract
Many aspects of metabolism are sex-biased, from gene expression in metabolic tissues to the prevalence and presentation of cardiometabolic diseases. The influence of hormones produced by male and female gonads has been widely documented, but recent studies have begun to elucidate the impact of genetic sex (XX or XY chromosomes) on cellular and organismal metabolism. XX and XY cells have differential gene dosage conferred by specific genes that escape X chromosome inactivation or the presence of Y chromosome genes that are absent from XX cells. Studies in mouse models that dissociate chromosomal and gonadal sex have uncovered mechanisms for sex-biased epigenetic, transcriptional, and post-transcriptional regulation of gene expression in conditions such as obesity, atherosclerosis, pulmonary hypertension, autoimmune disease, and Alzheimer's disease.
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Affiliation(s)
- Carrie B Wiese
- Department of Human Genetics, David Geffen School of Medicine, University of California, Los Angeles, CA 90095, USA
| | - Rozeta Avetisyan
- Department of Human Genetics, David Geffen School of Medicine, University of California, Los Angeles, CA 90095, USA
| | - Karen Reue
- Department of Human Genetics, David Geffen School of Medicine, University of California, Los Angeles, CA 90095, USA.
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4
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Sun L, Wang Z, Lu T, Manolio TA, Paterson AD. eXclusionarY: 10 years later, where are the sex chromosomes in GWASs? Am J Hum Genet 2023; 110:903-912. [PMID: 37267899 DOI: 10.1016/j.ajhg.2023.04.009] [Citation(s) in RCA: 4] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 06/04/2023] Open
Abstract
10 years ago, a detailed analysis showed that only 33% of genome-wide association study (GWAS) results included the X chromosome. Multiple recommendations were made to combat such exclusion. Here, we re-surveyed the research landscape to determine whether these earlier recommendations had been translated. Unfortunately, among the genome-wide summary statistics reported in 2021 in the NHGRI-EBI GWAS Catalog, only 25% provided results for the X chromosome and 3% for the Y chromosome, suggesting that the exclusion phenomenon not only persists but has also expanded into an exclusionary problem. Normalizing by physical length of the chromosome, the average number of studies published through November 2022 with genome-wide-significant findings on the X chromosome is ∼1 study/Mb. By contrast, it ranges from ∼6 to ∼16 studies/Mb for chromosomes 4 and 19, respectively. Compared with the autosomal growth rate of ∼0.086 studies/Mb/year over the last decade, studies of the X chromosome grew at less than one-seventh that rate, only ∼0.012 studies/Mb/year. Among the studies that reported significant associations on the X chromosome, we noted extreme heterogeneities in data analysis and reporting of results, suggesting the need for clear guidelines. Unsurprisingly, among the 430 scores sampled from the PolyGenic Score Catalog, 0% contained weights for sex chromosomal SNPs. To overcome the dearth of sex chromosome analyses, we provide five sets of recommendations and future directions. Finally, until the sex chromosomes are included in a whole-genome study, instead of GWASs, we propose such studies would more properly be referred to as "AWASs," meaning "autosome-wide scans."
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Affiliation(s)
- Lei Sun
- Department of Statistical Sciences, Faculty of Arts and Science, University of Toronto, Toronto, ON, Canada; Division of Biostatistics, Dalla Lana School of Public Health, University of Toronto, Toronto, ON, Canada.
| | - Zhong Wang
- Department of Statistics and Data Science, Faculty of Science, National University of Singapore, Singapore
| | - Tianyuan Lu
- Department of Statistical Sciences, Faculty of Arts and Science, University of Toronto, Toronto, ON, Canada; Lady Davis Institute for Medical Research, Jewish General Hospital, Montreal, QC, Canada
| | - Teri A Manolio
- Division of Genomic Medicine, National Human Genome Research Institute, NIH, Bethesda, MD, USA
| | - Andrew D Paterson
- Division of Biostatistics, Dalla Lana School of Public Health, University of Toronto, Toronto, ON, Canada; Division of Epidemiology, Dalla Lana School of Public Health, University of Toronto, Toronto, ON, Canada; Genetics and Genome Biology, The Hospital for Sick Children, Toronto, ON, Canada.
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5
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Yang ZY, Liu W, Yuan YX, Kong YF, Zhao PZ, Fung WK, Zhou JY. Robust association tests for quantitative traits on the X chromosome. Heredity (Edinb) 2022; 129:244-256. [PMID: 36085362 PMCID: PMC9519943 DOI: 10.1038/s41437-022-00560-y] [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: 07/15/2021] [Revised: 08/24/2022] [Accepted: 08/24/2022] [Indexed: 11/09/2022] Open
Abstract
The genome-wide association study is an elementary tool to assess the genetic contribution to complex human traits. However, such association tests are mainly proposed for autosomes, and less attention has been given to methods for identifying loci on the X chromosome due to their distinct biological features. In addition, the existing association tests for quantitative traits on the X chromosome either fail to incorporate the information of males or only detect variance heterogeneity. Therefore, we propose four novel methods, which are denoted as QXcat, QZmax, QMVXcat and QMVZmax. When using these methods, it is assumed that the risk alleles for females and males are the same and that the locus being studied satisfies the generalized genetic model for females. The first two methods are based on comparing the means of the trait value across different genotypes, while the latter two methods test for the difference of both means and variances. All four methods effectively incorporate the information of X chromosome inactivation. Simulation studies demonstrate that the proposed methods control the type I error rates well. Under the simulated scenarios, the proposed methods are generally more powerful than the existing methods. We also apply our proposed methods to data from the Minnesota Center for Twin and Family Research and find 10 single nucleotide polymorphisms that are statistically significantly associated with at least two traits at the significance level of 1 × 10-3.
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Affiliation(s)
- Zi-Ying Yang
- Department of Biostatistics, State Key Laboratory of Organ Failure Research, Ministry of Education, and Guangdong Provincial Key Laboratory of Tropical Disease Research, School of Public Health, Southern Medical University, Guangzhou, China
- Guangdong-Hong Kong-Macao Joint Laboratory for Contaminants Exposure and Health, Guangzhou, China
| | - Wei Liu
- Department of Biostatistics, State Key Laboratory of Organ Failure Research, Ministry of Education, and Guangdong Provincial Key Laboratory of Tropical Disease Research, School of Public Health, Southern Medical University, Guangzhou, China
- Guangdong-Hong Kong-Macao Joint Laboratory for Contaminants Exposure and Health, Guangzhou, China
| | - Yu-Xin Yuan
- Department of Biostatistics, State Key Laboratory of Organ Failure Research, Ministry of Education, and Guangdong Provincial Key Laboratory of Tropical Disease Research, School of Public Health, Southern Medical University, Guangzhou, China
- Guangdong-Hong Kong-Macao Joint Laboratory for Contaminants Exposure and Health, Guangzhou, China
| | - Yi-Fan Kong
- Department of Biostatistics, State Key Laboratory of Organ Failure Research, Ministry of Education, and Guangdong Provincial Key Laboratory of Tropical Disease Research, School of Public Health, Southern Medical University, Guangzhou, China
| | - Pei-Zhen Zhao
- Department of Biostatistics, State Key Laboratory of Organ Failure Research, Ministry of Education, and Guangdong Provincial Key Laboratory of Tropical Disease Research, School of Public Health, Southern Medical University, Guangzhou, China
| | - Wing Kam Fung
- Department of Statistics and Actuarial Science, The University of Hong Kong, Hong Kong, China.
| | - Ji-Yuan Zhou
- Department of Biostatistics, State Key Laboratory of Organ Failure Research, Ministry of Education, and Guangdong Provincial Key Laboratory of Tropical Disease Research, School of Public Health, Southern Medical University, Guangzhou, China.
- Guangdong-Hong Kong-Macao Joint Laboratory for Contaminants Exposure and Health, Guangzhou, China.
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Keur N, Ricaño-Ponce I, Kumar V, Matzaraki V. A systematic review of analytical methods used in genetic association analysis of the X-chromosome. Brief Bioinform 2022; 23:6651325. [PMID: 35901513 DOI: 10.1093/bib/bbac287] [Citation(s) in RCA: 4] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/06/2022] [Revised: 06/07/2022] [Accepted: 06/23/2022] [Indexed: 11/13/2022] Open
Abstract
Genetic association studies have been very successful at elucidating the genetic background of many complex diseases/traits. However, the X-chromosome is often neglected in these studies because of technical difficulties and the fact that most tools only utilize genetic data from autosomes. In this review, we aim to provide an overview of different practical approaches that are followed to incorporate the X-chromosome in association analysis, such as Genome-Wide Association Studies and Expression Quantitative Trait Loci Analysis. In general, the choice of which test statistics is most appropriate will depend on three main criteria: (1) the underlying X-inactivation model, (2) if Hardy-Weinberg equilibrium holds and sex-specific allele frequencies are expected and (3) whether adjustment for confounding variables is required. All in all, it is recommended that a combination of different association tests should be used for the analysis of X-chromosome.
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Affiliation(s)
- Nick Keur
- Department of Internal Medicine and Radboud Center for Infectious Diseases, Radboud University Medical Center, Geert Grooteplein Zuid 10, 6525 HP, Nijmegen, The Netherlands
| | - Isis Ricaño-Ponce
- Department of Internal Medicine and Radboud Center for Infectious Diseases, Radboud University Medical Center, Geert Grooteplein Zuid 10, 6525 HP, Nijmegen, The Netherlands
| | - Vinod Kumar
- Department of Internal Medicine and Radboud Center for Infectious Diseases, Radboud University Medical Center, Geert Grooteplein Zuid 10, 6525 HP, Nijmegen, The Netherlands.,Department of Genetics, University of Groningen, University Medical Center Groningen, Hanzeplein 1, 9700RB, Groningen, The Netherlands
| | - Vasiliki Matzaraki
- Department of Internal Medicine and Radboud Center for Infectious Diseases, Radboud University Medical Center, Geert Grooteplein Zuid 10, 6525 HP, Nijmegen, The Netherlands
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7
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Wang Z, Sun L, Paterson AD. Major sex differences in allele frequencies for X chromosomal variants in both the 1000 Genomes Project and gnomAD. PLoS Genet 2022; 18:e1010231. [PMID: 35639794 PMCID: PMC9187127 DOI: 10.1371/journal.pgen.1010231] [Citation(s) in RCA: 2] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/03/2021] [Revised: 06/10/2022] [Accepted: 05/03/2022] [Indexed: 12/19/2022] Open
Abstract
An unexpectedly high proportion of SNPs on the X chromosome in the 1000 Genomes Project phase 3 data were identified with significant sex differences in minor allele frequencies (sdMAF). sdMAF persisted for many of these SNPs in the recently released high coverage whole genome sequence of the 1000 Genomes Project that was aligned to GRCh38, and it was consistent between the five super-populations. Among the 245,825 common (MAF>5%) biallelic X-chromosomal SNPs in the phase 3 data presumed to be of high quality, 2,039 have genome-wide significant sdMAF (p-value <5e-8). sdMAF varied by location: non-pseudo-autosomal region (NPR) = 0.83%, pseudo-autosomal regions (PAR1) = 0.29%, PAR2 = 13.1%, and X-transposed region (XTR)/PAR3 = 0.85% of SNPs had sdMAF, and they were clustered at the NPR-PAR boundaries, among others. sdMAF at the NPR-PAR boundaries are biologically expected due to sex-linkage, but have generally been ignored in association studies. For comparison, similar analyses found only 6, 1 and 0 SNPs with significant sdMAF on chromosomes 1, 7 and 22, respectively. Similar sdMAF results for the X chromosome were obtained from the high coverage whole genome sequence data from gnomAD V 3.1.2 for both the non-Finnish European and African/African American samples. Future X chromosome analyses need to take sdMAF into account. The human X chromosome contains over 800 genes and is the 8th largest human chromosome. Genome-wide associations studies have generally failed to examine variants on the X chromosome for association with diseases and traits, partly due to complexities of the data analysis, and challenges with genotype imputation. We examined X chromosomal variants from the 1000 Genomes Project for sex differences in allele frequency and found that many variants showed significant differences. These variants cluster at the centromeric parts of the pseudoautosomal regions 1 and 2, as well as the putative pseudo-autosomal region 3 (also termed X-transposed region). This pattern was observed in high coverage whole genome sequence data from the same subjects that was aligned to GRCh38, suggesting that is not an artefact of low coverage sequencing or problems specific to GRCh37. In addition, we replicated this phenomenon in high coverage whole genome sequence aligned to GRCh38 from the gnomAD database in both the non-Finnish European and African/African American samples. These findings have implications for the analysis of X chromosomal variants for disease and trait associations.
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Affiliation(s)
- Zhong Wang
- Department of Statistics and Data Science, Faculty of Science, National University of Singapore, Singapore
| | - Lei Sun
- Department of Statistic Sciences, Faculty of Arts and Science, University of Toronto, Ontario, Canada
- Biostatistics Division, Dalla Lana School of Public Health, University of Toronto, Ontario, Canada
- * E-mail: (LS); (ADP)
| | - Andrew D. Paterson
- Biostatistics Division, Dalla Lana School of Public Health, University of Toronto, Ontario, Canada
- Genetics and Genome Biology, The Hospital for Sick Children, Ontario, Canada
- Epidemiology Division, Dalla Lana School of Public Health, University of Toronto, Ontario, Canada
- * E-mail: (LS); (ADP)
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8
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Barbier M, Camuzat A, Hachimi KE, Guegan J, Rinaldi D, Lattante S, Houot M, Sánchez-Valle R, Sabatelli M, Antonell A, Molina-Porcel L, Clot F, Couratier P, van der Ende E, van der Zee J, Manzoni C, Camu W, Cazeneuve C, Sellal F, Didic M, Golfier V, Pasquier F, Duyckaerts C, Rossi G, Bruni AC, Alvarez V, Gómez-Tortosa E, de Mendonça A, Graff C, Masellis M, Nacmias B, Oumoussa BM, Jornea L, Forlani S, Van Deerlin V, Rohrer JD, Gelpi E, Rademakers R, Van Swieten J, Le Guern E, Van Broeckhoven C, Ferrari R, Génin E, Brice A, Le Ber I. SLITRK2, an X-linked modifier of the age at onset in C9orf72 frontotemporal lobar degeneration. Brain 2021; 144:2798-2811. [PMID: 34687211 DOI: 10.1093/brain/awab171] [Citation(s) in RCA: 5] [Impact Index Per Article: 1.7] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/21/2020] [Revised: 03/05/2021] [Accepted: 04/01/2021] [Indexed: 11/13/2022] Open
Abstract
The G4C2-repeat expansion in C9orf72 is the most common cause of frontotemporal dementia and of amyotrophic lateral sclerosis. The variability of age at onset and phenotypic presentations is a hallmark of C9orf72 disease. In this study, we aimed to identify modifying factors of disease onset in C9orf72 carriers using a family-based approach, in pairs of C9orf72 carrier relatives with concordant or discordant age at onset. Linkage and association analyses provided converging evidence for a locus on chromosome Xq27.3. The minor allele A of rs1009776 was associated with an earlier onset (P = 1 × 10-5). The association with onset of dementia was replicated in an independent cohort of unrelated C9orf72 patients (P = 0.009). The protective major allele delayed the onset of dementia from 5 to 13 years on average depending on the cohort considered. The same trend was observed in an independent cohort of C9orf72 patients with extreme deviation of the age at onset (P = 0.055). No association of rs1009776 was detected in GRN patients, suggesting that the effect of rs1009776 was restricted to the onset of dementia due to C9orf72. The minor allele A is associated with a higher SLITRK2 expression based on both expression quantitative trait loci (eQTL) databases and in-house expression studies performed on C9orf72 brain tissues. SLITRK2 encodes for a post-synaptic adhesion protein. We further show that synaptic vesicle glycoprotein 2 and synaptophysin, two synaptic vesicle proteins, were decreased in frontal cortex of C9orf72 patients carrying the minor allele. Upregulation of SLITRK2 might be associated with synaptic dysfunctions and drives adverse effects in C9orf72 patients that could be modulated in those carrying the protective allele. How the modulation of SLITRK2 expression affects synaptic functions and influences the disease onset of dementia in C9orf72 carriers will require further investigations. In summary, this study describes an original approach to detect modifier genes in rare diseases and reinforces rising links between C9orf72 and synaptic dysfunctions that might directly influence the occurrence of first symptoms.
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Affiliation(s)
- Mathieu Barbier
- Sorbonne Université, Institut du Cerveau-Paris Brain Institute-ICM, Hôpital de la Salpêtrière, Inserm U 1127, CNRS UMR 7225, 75013, Paris, France
| | - Agnès Camuzat
- Sorbonne Université, Institut du Cerveau-Paris Brain Institute-ICM, Hôpital de la Salpêtrière, Inserm U 1127, CNRS UMR 7225, 75013, Paris, France
| | - Khalid El Hachimi
- Sorbonne Université, Institut du Cerveau-Paris Brain Institute-ICM, Hôpital de la Salpêtrière, Inserm U 1127, CNRS UMR 7225, 75013, Paris, France
| | - Justine Guegan
- Sorbonne Université, Institut du Cerveau-Paris Brain Institute-ICM, Hôpital de la Salpêtrière, Inserm U 1127, CNRS UMR 7225, 75013, Paris, France
| | - Daisy Rinaldi
- Sorbonne Université, Institut du Cerveau-Paris Brain Institute-ICM, Hôpital de la Salpêtrière, Inserm U 1127, CNRS UMR 7225, 75013, Paris, France
- Center for Rare or Early-Onset Dementias, IM2A, Département de Neurologie, AP-HP-Hôpital Pitié-Salpêtrière, Paris, France
| | - Serena Lattante
- Sezione di Medicina Genomica, Dipartimento Scienze della Vita e Sanità Pubblica, Facoltà di Medicina e Chirurgia, Università Cattolica Sacro Cuore; U.O.C. Genetica Medica, Dipartimento di Scienze di Laboratorio e Infettivologico, Fondazione Policlinico Universitario 'A. Gemelli' IRCCS, Rome, Italy
| | - Marion Houot
- Sorbonne Université, Institut du Cerveau-Paris Brain Institute-ICM, Hôpital de la Salpêtrière, Inserm U 1127, CNRS UMR 7225, 75013, Paris, France
- Center for Rare or Early-Onset Dementias, IM2A, Département de Neurologie, AP-HP-Hôpital Pitié-Salpêtrière, Paris, France
- Centre of Excellence of Neurodegenerative Disease (CoEN), Hôpital Pitié-Salpêtrière, Paris, France
| | - Raquel Sánchez-Valle
- Alzheimer's Disease and Other Cognitive Disorders Unit, Neurology Service, Hospital Clínic, Institut d'Investigacions Biomèdiques August Pi i Sunyer (IDIBAPS), University of Barcelona, 08036 Barcelona, Catalunya, Spain
| | - Mario Sabatelli
- Adult NEMO Clinical Center, Unit of Neurology, Department of Aging, Neurological, Orthopedic and Head-Neck Sciences, Fondazione Policlinico Universitario A. Gemelli IRCCS, 00168 Rome, Italy
- Section of Neurology, Department of Neuroscience, Faculty of Medicine and Surgery, Università Cattolica del Sacro Cuore, 00168 Rome, Italy
| | - Anna Antonell
- Alzheimer's Disease and Other Cognitive Disorders Unit, Neurology Service, Hospital Clínic, Institut d'Investigacions Biomèdiques August Pi i Sunyer (IDIBAPS), University of Barcelona, 08036 Barcelona, Catalunya, Spain
| | - Laura Molina-Porcel
- Alzheimer's Disease and Other Cognitive Disorders Unit, Neurology Service, Hospital Clínic, Institut d'Investigacions Biomèdiques August Pi i Sunyer (IDIBAPS), University of Barcelona, 08036 Barcelona, Catalunya, Spain
- Neurological Tissue Bank of the Biobank-Hospital Clinic-IDIBAPS, Barcelona, Catalunya, Spain
| | - Fabienne Clot
- Unité Fonctionnelle de Neurogénétique Moléculaire et Cellulaire, Département de Génétique et Cytogénétique, AP-HP, Hôpitaux Universitaires La Pitié Salpêtrière-Charles Foix, Paris, France
| | | | - Emma van der Ende
- Department of Neurology, Erasmus University Medical Center, 3015 GD Rotterdam, The Netherlands
| | - Julie van der Zee
- Neurodegenerative Brain Diseases Group, VIB Center for Molecular Neurology, VIB, Antwerp, Belgium
- Department of Biomedical Sciences, University of Antwerp, Antwerp, Belgium
| | | | - William Camu
- Reference Centre for ALS, University Hospital Gui de Chauliac, University of Montpellier, Montpellier, France
| | - Cécile Cazeneuve
- Unité Fonctionnelle de Neurogénétique Moléculaire et Cellulaire, Département de Génétique et Cytogénétique, AP-HP, Hôpitaux Universitaires La Pitié Salpêtrière-Charles Foix, Paris, France
| | - François Sellal
- Neurology Department, Hôpitaux Civils de Colmar, France
- INSERM U-1118, Strasbourg University, Strasbourg, France
| | - Mira Didic
- APHM, Timone, Service de Neurologie et Neuropsychologie, Hôpital Timone Adultes, Marseille, France
- Aix Marseille Univ, INSERM, INS, Inst Neurosci Syst, Marseille, France
| | - Véronique Golfier
- Service de Neurologie, Centre Hospitalier Yves Le Foll, Saint Brieuc, France
| | - Florence Pasquier
- University of Lille, Inserm UMRS1172, CHU, DISTAlz, LiCEND, F-59000 Lille, France
| | - Charles Duyckaerts
- Sorbonne Université, Institut du Cerveau-Paris Brain Institute-ICM, Hôpital de la Salpêtrière, Inserm U 1127, CNRS UMR 7225, 75013, Paris, France
- Laboratoire de Neuropathologie Escourolle, Hôpital de la Pitié-Salpêtrière, AP-HP, Paris, France
| | - Giacomina Rossi
- Division of Neurology V and Neuropathology; Fondazione IRCCS Istituto Neurologico Carlo Besta, Milano, Italy
| | - Amalia C Bruni
- Regional Neurogenetic Centre, Department of Primary Care, ASP-CZ, Catanzaro, Italy
| | - Victoria Alvarez
- Laboratorio de Genética- Hospital Universitario Central de Asturias, Oviedo, Spain
- Instituto de INvestigación Biosanitaria del Principado de Asturias (ISPA), Oviedo, Spain
| | | | | | - Caroline Graff
- Department of Geriatric Medicine, Karolinska University Hospital-Huddinge, Stockholm, Sweden
| | - Mario Masellis
- Hurvitz Brain Sciences Program, Sunnybrook Research Institute; Department of Medicine (Neurology), Sunnybrook Health Sciences Centre, University of Toronto, Toronto, Canada
| | - Benedetta Nacmias
- Department of Neuroscience, Psychology, Drug Research and Child Health, University of Florence, Florence, Italy
- IRCCS Fondazione Don Carlo Gnocchi, Florence, Italy
| | - Badreddine Mohand Oumoussa
- Sorbonne Université, Inserm, UMS Production et Analyse des données en Sciences de la vie et en Santé, PASS, Plateforme Post-génomique de la Pitié-Salpêtrière, P3S, F-75013, Paris, France
| | - Ludmila Jornea
- Sorbonne Université, Institut du Cerveau-Paris Brain Institute-ICM, Hôpital de la Salpêtrière, Inserm U 1127, CNRS UMR 7225, 75013, Paris, France
| | - Sylvie Forlani
- Sorbonne Université, Institut du Cerveau-Paris Brain Institute-ICM, Hôpital de la Salpêtrière, Inserm U 1127, CNRS UMR 7225, 75013, Paris, France
| | | | - Viviana Van Deerlin
- Department of Pathology and Laboratory Medicine, University of Pennsylvania, Philadelphia, PA 19104, USA
| | - Jonathan D Rohrer
- Dementia Research Centre, Department of Neurodegenerative Disease, UCL Queen Square Institute of Neurology, University College London, London, UK
| | - Ellen Gelpi
- Neurological Tissue Bank of the Biobank-Hospital Clinic-IDIBAPS, Barcelona, Catalunya, Spain
- Division of Neuropathology and Neurochemistry, Department of Neurology, Medical University of Vienna, Vienna, Austria
| | - Rosa Rademakers
- Neurodegenerative Brain Diseases Group, VIB Center for Molecular Neurology, VIB, Antwerp, Belgium
| | - John Van Swieten
- Department of Neurology, Erasmus University Medical Center, 3015 GD Rotterdam, The Netherlands
| | - Eric Le Guern
- Unité Fonctionnelle de Neurogénétique Moléculaire et Cellulaire, Département de Génétique et Cytogénétique, AP-HP, Hôpitaux Universitaires La Pitié Salpêtrière-Charles Foix, Paris, France
| | - Christine Van Broeckhoven
- Neurodegenerative Brain Diseases Group, VIB Center for Molecular Neurology, VIB, Antwerp, Belgium
- Department of Biomedical Sciences, University of Antwerp, Antwerp, Belgium
| | | | - Emmanuelle Génin
- Génétique, Génomique Fonctionnelle et Biotechnologies, Faculté de Médecine, Univ Brest, Inserm UMR1078, Brest, France
| | - Alexis Brice
- Sorbonne Université, Institut du Cerveau-Paris Brain Institute-ICM, Hôpital de la Salpêtrière, Inserm U 1127, CNRS UMR 7225, 75013, Paris, France
| | - Isabelle Le Ber
- Sorbonne Université, Institut du Cerveau-Paris Brain Institute-ICM, Hôpital de la Salpêtrière, Inserm U 1127, CNRS UMR 7225, 75013, Paris, France
- Center for Rare or Early-Onset Dementias, IM2A, Département de Neurologie, AP-HP-Hôpital Pitié-Salpêtrière, Paris, France
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9
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Chen B, Craiu RV, Strug LJ, Sun L. The X factor: A robust and powerful approach to X-chromosome-inclusive whole-genome association studies. Genet Epidemiol 2021; 45:694-709. [PMID: 34224641 PMCID: PMC9292551 DOI: 10.1002/gepi.22422] [Citation(s) in RCA: 13] [Impact Index Per Article: 4.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/03/2021] [Revised: 05/14/2021] [Accepted: 05/28/2021] [Indexed: 12/17/2022]
Abstract
The X‐chromosome is often excluded from genome‐wide association studies because of analytical challenges. Some of the problems, such as the random, skewed, or no X‐inactivation model uncertainty, have been investigated. Other considerations have received little to no attention, such as the value in considering nonadditive and gene–sex interaction effects, and the inferential consequence of choosing different baseline alleles (i.e., the reference vs. the alternative allele). Here we propose a unified and flexible regression‐based association test for X‐chromosomal variants. We provide theoretical justifications for its robustness in the presence of various model uncertainties, as well as for its improved power when compared with the existing approaches under certain scenarios. For completeness, we also revisit the autosomes and show that the proposed framework leads to a more robust approach than the standard method. Finally, we provide supporting evidence by revisiting several published association studies. Supporting Information for this article are available online.
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Affiliation(s)
- Bo Chen
- Department of Statistical Sciences, University of Toronto, Toronto, Ontario, Canada
| | - Radu V Craiu
- Department of Statistical Sciences, University of Toronto, Toronto, Ontario, Canada
| | - Lisa J Strug
- Department of Statistical Sciences, University of Toronto, Toronto, Ontario, Canada.,Biostatistics Division, Dalla Lana School of Public Health, University of Toronto, Toronto, Ontario, Canada.,Department of Computer Science, University of Toronto, Toronto, Ontario, Canada.,Program in Genetics and Genome Biology, The Hospital for Sick Children, Toronto, Ontario, Canada
| | - Lei Sun
- Department of Statistical Sciences, University of Toronto, Toronto, Ontario, Canada.,Biostatistics Division, Dalla Lana School of Public Health, University of Toronto, Toronto, Ontario, Canada
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10
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Hong EP, Chao MJ, Massey T, McAllister B, Lobanov S, Jones L, Holmans P, Kwak S, Orth M, Ciosi M, Monckton DG, Long JD, Lucente D, Wheeler VC, MacDonald ME, Gusella JF, Lee JM. Association Analysis of Chromosome X to Identify Genetic Modifiers of Huntington's Disease. J Huntingtons Dis 2021; 10:367-375. [PMID: 34180418 DOI: 10.3233/jhd-210485] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/05/2023]
Abstract
BACKGROUND Huntington's disease (HD) is caused by an expanded (>35) CAG trinucleotide repeat in huntingtin (HTT). Age-at-onset of motor symptoms is inversely correlated with the size of the inherited CAG repeat, which expands further in brain regions due to somatic repeat instability. Our recent genetic investigation focusing on autosomal SNPs revealed that age-at-onset is also influenced by genetic variation at many loci, the majority of which encode genes involved in DNA maintenance/repair processes and repeat instability. OBJECTIVE We performed a complementary association analysis to determine whether variants in the X chromosome modify HD. METHODS We imputed SNPs on chromosome X for ∼9,000 HD subjects of European ancestry and performed an X chromosome-wide association study (XWAS) to test for association with age-at-onset corrected for inherited CAG repeat length. RESULTS In a mixed effects model XWAS analysis of all subjects (males and females), assuming random X-inactivation in females, no genome-wide significant onset modification signal was found. However, suggestive significant association signals were detected at Xq12 (top SNP, rs59098970; p-value, 1.4E-6), near moesin (MSN), in a region devoid of DNA maintenance genes. Additional suggestive signals not involving DNA repair genes were observed in male- and female-only analyses at other locations. CONCLUSION Although not genome-wide significant, potentially due to small effect size compared to the power of the current study, our data leave open the possibility of modification of HD by a non-DNA repair process. Our XWAS results are publicly available at the updated GEM EURO 9K website hosted at https://www.hdinhd.org/ for browsing, pathway analysis, and data download.
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Affiliation(s)
- Eun Pyo Hong
- Molecular Neurogenetics Unit, Center for Genomic Medicine, Massachusetts General Hospital, Boston, MA, USA.,Department of Neurology, Harvard Medical School, Boston, MA, USA.,Medical and Population Genetics Program, the Broad Institute of M.I.T. and Harvard, Cambridge, MA, USA
| | - Michael J Chao
- Molecular Neurogenetics Unit, Center for Genomic Medicine, Massachusetts General Hospital, Boston, MA, USA.,Department of Neurology, Harvard Medical School, Boston, MA, USA
| | - Thomas Massey
- Medical Research Council Centre for Neuropsychiatric Genetics and Genomics, Institute of Psychological Medicine and Clinical Neurosciences, School of Medicine, Cardiff University, Cardiff, UK
| | - Branduff McAllister
- Medical Research Council Centre for Neuropsychiatric Genetics and Genomics, Institute of Psychological Medicine and Clinical Neurosciences, School of Medicine, Cardiff University, Cardiff, UK
| | - Sergey Lobanov
- Medical Research Council Centre for Neuropsychiatric Genetics and Genomics, Institute of Psychological Medicine and Clinical Neurosciences, School of Medicine, Cardiff University, Cardiff, UK
| | - Lesley Jones
- Medical Research Council Centre for Neuropsychiatric Genetics and Genomics, Institute of Psychological Medicine and Clinical Neurosciences, School of Medicine, Cardiff University, Cardiff, UK
| | - Peter Holmans
- Medical Research Council Centre for Neuropsychiatric Genetics and Genomics, Institute of Psychological Medicine and Clinical Neurosciences, School of Medicine, Cardiff University, Cardiff, UK
| | | | - Michael Orth
- Department of Old Age Psychiatry and Psychotherapy, University of Bern, Switzerland
| | - Marc Ciosi
- Institute of Molecular, Cell and Systems Biology, College of Medical, Veterinary and Life Sciences, University of Glasgow, Glasgow, UK
| | - Darren G Monckton
- Institute of Molecular, Cell and Systems Biology, College of Medical, Veterinary and Life Sciences, University of Glasgow, Glasgow, UK
| | - Jeffrey D Long
- Department of Psychiatry, Carver College of Medicine and Department of Biostatistics, College of Public Health, and Department of Psychiatry, Carver College of Medicine, University of Iowa, Iowa City, Iowa, USA
| | - Diane Lucente
- Molecular Neurogenetics Unit, Center for Genomic Medicine, Massachusetts General Hospital, Boston, MA, USA
| | - Vanessa C Wheeler
- Molecular Neurogenetics Unit, Center for Genomic Medicine, Massachusetts General Hospital, Boston, MA, USA.,Department of Neurology, Harvard Medical School, Boston, MA, USA.,Medical and Population Genetics Program, the Broad Institute of M.I.T. and Harvard, Cambridge, MA, USA
| | - Marcy E MacDonald
- Molecular Neurogenetics Unit, Center for Genomic Medicine, Massachusetts General Hospital, Boston, MA, USA.,Department of Neurology, Harvard Medical School, Boston, MA, USA.,Medical and Population Genetics Program, the Broad Institute of M.I.T. and Harvard, Cambridge, MA, USA
| | - James F Gusella
- Molecular Neurogenetics Unit, Center for Genomic Medicine, Massachusetts General Hospital, Boston, MA, USA.,Medical and Population Genetics Program, the Broad Institute of M.I.T. and Harvard, Cambridge, MA, USA.,Department of Genetics, Blavatnik Institute, Harvard Medical School, Boston, MA, USA
| | - Jong-Min Lee
- Molecular Neurogenetics Unit, Center for Genomic Medicine, Massachusetts General Hospital, Boston, MA, USA.,Department of Neurology, Harvard Medical School, Boston, MA, USA.,Medical and Population Genetics Program, the Broad Institute of M.I.T. and Harvard, Cambridge, MA, USA
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11
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Song Y, Biernacka JM, Winham SJ. Testing and estimation of X-chromosome SNP effects: Impact of model assumptions. Genet Epidemiol 2021; 45:577-592. [PMID: 34082482 PMCID: PMC8453908 DOI: 10.1002/gepi.22393] [Citation(s) in RCA: 5] [Impact Index Per Article: 1.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/15/2020] [Revised: 04/30/2021] [Accepted: 05/18/2021] [Indexed: 12/16/2022]
Abstract
Interest in analyzing X chromosome single nucleotide polymorphisms (SNPs) is growing and several approaches have been proposed. Prior studies have compared power of different approaches, but bias and interpretation of coefficients have received less attention. We performed simulations to demonstrate the impact of X chromosome model assumptions on effect estimates. We investigated the coefficient biases of SNP and sex effects with commonly used models for X chromosome SNPs, including models with and without assumptions of X chromosome inactivation (XCI), and with and without SNP–sex interaction terms. Sex and SNP coefficient biases were observed when assumptions made about XCI and sex differences in SNP effect in the analysis model were inconsistent with the data‐generating model. However, including a SNP–sex interaction term often eliminated these biases. To illustrate these findings, estimates under different genetic model assumptions are compared and interpreted in a real data example. Models to analyze X chromosome SNPs make assumptions beyond those made in autosomal variant analysis. Assumptions made about X chromosome SNP effects should be stated clearly when reporting and interpreting X chromosome associations. Fitting models with SNP × Sex interaction terms can avoid reliance on assumptions, eliminating coefficient bias even in the absence of sex differences in SNP effect.
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Affiliation(s)
- Yilin Song
- Department of Biostatistics, University of Washington, Seattle, Washington, USA.,Department of Mathematics, Statistics, and Computer Science, St. Olaf College, Northfield, Minnesota, USA
| | - Joanna M Biernacka
- Division of Computational Biology, Department of Quantitative Health Sciences, Mayo Clinic, Rochester, Minnesota, USA
| | - Stacey J Winham
- Division of Computational Biology, Department of Quantitative Health Sciences, Mayo Clinic, Rochester, Minnesota, USA
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12
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Smith SM, Douaud G, Chen W, Hanayik T, Alfaro-Almagro F, Sharp K, Elliott LT. An expanded set of genome-wide association studies of brain imaging phenotypes in UK Biobank. Nat Neurosci 2021; 24:737-745. [PMID: 33875891 PMCID: PMC7610742 DOI: 10.1038/s41593-021-00826-4] [Citation(s) in RCA: 144] [Impact Index Per Article: 48.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/27/2020] [Accepted: 02/23/2021] [Indexed: 02/01/2023]
Abstract
UK Biobank is a major prospective epidemiological study, including multimodal brain imaging, genetics and ongoing health outcomes. Previously, we published genome-wide associations of 3,144 brain imaging-derived phenotypes, with a discovery sample of 8,428 individuals. Here we present a new open resource of genome-wide association study summary statistics, using the 2020 data release, almost tripling the discovery sample size. We now include the X chromosome and new classes of imaging-derived phenotypes (subcortical volumes and tissue contrast). Previously, we found 148 replicated clusters of associations between genetic variants and imaging phenotypes; in this study, we found 692, including 12 on the X chromosome. We describe some of the newly found associations, focusing on the X chromosome and autosomal associations involving the new classes of imaging-derived phenotypes. Our novel associations implicate, for example, pathways involved in the rare X-linked STAR (syndactyly, telecanthus and anogenital and renal malformations) syndrome, Alzheimer's disease and mitochondrial disorders.
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Affiliation(s)
- Stephen M Smith
- Wellcome Centre for Integrative Neuroimaging (WIN FMRIB), University of Oxford, Oxford, United Kingdom
| | - Gwenaëlle Douaud
- Wellcome Centre for Integrative Neuroimaging (WIN FMRIB), University of Oxford, Oxford, United Kingdom
| | - Winfield Chen
- Department of Statistics and Actuarial Science, Simon Fraser University, Burnaby BC, Canada
| | - Taylor Hanayik
- Wellcome Centre for Integrative Neuroimaging (WIN FMRIB), University of Oxford, Oxford, United Kingdom
| | - Fidel Alfaro-Almagro
- Wellcome Centre for Integrative Neuroimaging (WIN FMRIB), University of Oxford, Oxford, United Kingdom
| | | | - Lloyd T Elliott
- Department of Statistics and Actuarial Science, Simon Fraser University, Burnaby BC, Canada.
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13
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Turkmen AS, Lin S. Detecting X-linked common and rare variant effects in family-based sequencing studies. Genet Epidemiol 2020; 45:36-45. [PMID: 32864779 DOI: 10.1002/gepi.22352] [Citation(s) in RCA: 4] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/23/2019] [Revised: 06/26/2020] [Accepted: 08/03/2020] [Indexed: 11/08/2022]
Abstract
The breakthroughs in next generation sequencing have allowed us to access data consisting of both common and rare variants, and in particular to investigate the impact of rare genetic variation on complex diseases. Although rare genetic variants are thought to be important components in explaining genetic mechanisms of many diseases, discovering these variants remains challenging, and most studies are restricted to population-based designs. Further, despite the shift in the field of genome-wide association studies (GWAS) towards studying rare variants due to the "missing heritability" phenomenon, little is known about rare X-linked variants associated with complex diseases. For instance, there is evidence that X-linked genes are highly involved in brain development and cognition when compared with autosomal genes; however, like most GWAS for other complex traits, previous GWAS for mental diseases have provided poor resources to deal with identification of rare variant associations on X-chromosome. In this paper, we address the two issues described above by proposing a method that can be used to test X-linked variants using sequencing data on families. Our method is much more general than existing methods, as it can be applied to detect both common and rare variants, and is applicable to autosomes as well. Our simulation study shows that the method is efficient, and exhibits good operational characteristics. An application to the University of Miami Study on Genetics of Autism and Related Disorders also yielded encouraging results.
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Affiliation(s)
- Asuman S Turkmen
- Statistics Department, The Ohio State University, Columbus, Ohio.,Statistics Department, The Ohio State University, Newark, Ohio
| | - Shili Lin
- Statistics Department, The Ohio State University, Columbus, Ohio
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14
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Deng WQ, Mao S, Kalnapenkis A, Esko T, Mägi R, Paré G, Sun L. Analytical strategies to include the X-chromosome in variance heterogeneity analyses: Evidence for trait-specific polygenic variance structure. Genet Epidemiol 2019; 43:815-830. [PMID: 31332826 DOI: 10.1002/gepi.22247] [Citation(s) in RCA: 11] [Impact Index Per Article: 2.2] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/13/2019] [Revised: 06/07/2019] [Accepted: 06/13/2019] [Indexed: 12/12/2022]
Abstract
Genotype-stratified variance of a quantitative trait could differ in the presence of gene-gene or gene-environment interactions. Genetic markers associated with phenotypic variance are thus considered promising candidates for follow-up interaction or joint location-scale analyses. However, as in studies of main effects, the X-chromosome is routinely excluded from "whole-genome" scans due to analytical challenges. Specifically, as males carry only one copy of the X-chromosome, the inherent sex-genotype dependency could bias the trait-genotype association, through sexual dimorphism in quantitative traits with sex-specific means or variances. Here we investigate phenotypic variance heterogeneity associated with X-chromosome single nucleotide polymorphisms (SNPs) and propose valid and powerful strategies. Among those, a generalized Levene's test has adequate power and remains robust to sexual dimorphism. An alternative approach is a sex-stratified analysis but at the cost of slightly reduced power and modeling flexibility. We applied both methods to an Estonian study of gene expression quantitative trait loci (eQTL; n = 841), and two complex trait studies of height, hip, and waist circumferences, and body mass index from Multi-Ethnic Study of Atherosclerosis (MESA; n = 2,073) and UK Biobank (UKB; n = 327,393). Consistent with previous eQTL findings on mean, we found some but no conclusive evidence for cis regulators being enriched for variance association. SNP rs2681646 is associated with variance of waist circumference (p = 9.5E-07) at X-chromosome-wide significance in UKB, with a suggestive female-specific effect in MESA (p = 0.048). Collectively, an enrichment analysis using permutated UKB (p < 0.1) and MESA (p < 0.01) datasets, suggests a possible polygenic structure for the variance of human height.
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Affiliation(s)
- Wei Q Deng
- Department of Statistical Sciences, Faculty of Arts and Science, University of Toronto, Toronto, Canada
| | - Shihong Mao
- Department of Pathology and Molecular Medicine, Population Health Research Institute, Hamilton Health Sciences and McMaster University, Hamilton, Canada
| | - Anette Kalnapenkis
- Estonian Genome Center, Institute of Genomics, University of Tartu, Tartu, Estonia
| | - Tõnu Esko
- Estonian Genome Center, Institute of Genomics, University of Tartu, Tartu, Estonia.,Program in Medical and Population Genetics, Broad Institute, Cambridge, Massachusetts
| | - Reedik Mägi
- Estonian Genome Center, Institute of Genomics, University of Tartu, Tartu, Estonia
| | - Guillaume Paré
- Department of Pathology and Molecular Medicine, Population Health Research Institute, Hamilton Health Sciences and McMaster University, Hamilton, Canada.,Department of Pathology and Molecular Medicine, McMaster University, Hamilton, Canada
| | - Lei Sun
- Department of Statistical Sciences, Faculty of Arts and Science, University of Toronto, Toronto, Canada.,Division of Biostatistics, Dalla Lana School of Public Health, University of Toronto, Toronto, Canada
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