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Gómez-Pinedo U, Torre-Fuentes L, Matías-Guiu JA, Pytel V, Ojeda-Hernández DD, Selma-Calvo B, Montero-Escribano P, Vidorreta-Ballesteros L, Matías-Guiu J. Exonic variants of the P2RX7 gene in familial multiple sclerosis. Neurologia 2022:S2173-5808(22)00189-4. [PMID: 36470550 DOI: 10.1016/j.nrleng.2022.12.001] [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: 09/09/2022] [Accepted: 10/09/2022] [Indexed: 12/12/2022] Open
Abstract
INTRODUCTION Several studies have analysed the presence of P2RX7 variants in patients with MS, reporting diverging results. METHODS Our study analyses P2RX7 variants detected through whole-exome sequencing (WES). RESULTS We analysed P2RX7, P2RX4, and CAMKK2 gene variants detected by whole-exome sequencing in all living members (n = 127) of 21 families including at least 2 individuals with multiple sclerosis. P2RX7 gene polymorphisms previously associated with autoimmune disease. Although no differences were observed between individuals with and without multiple sclerosis, we found greater polymorphism of gain-of-function variants of P2RX7 in families with individuals with multiple sclerosis than in the general population. Copresence of gain-of-function and loss-of-function variants was not observed to reduce the risk of presenting the disease. Three families displayed heterozygous gain-of-function SNPs in patients with multiple sclerosis but not in healthy individuals. We were unable to determine the impact of copresence of P2RX4 and CAMKK2 variants with P2RX7 variants, or the potential effect of the different haplotypes described in the gene. No clinical correlations with other autoimmune diseases were observed in our cohort. CONCLUSIONS Our results support the hypothesis that the disease is polygenic and point to a previously unknown mechanism of genetic predisposition to familial forms of multiple sclerosis. P2RX7 gene activity can be modified, which suggests the possibility of preventive pharmacological treatments for families including patients with familial multiple sclerosis.
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Affiliation(s)
- U Gómez-Pinedo
- Laboratory of Neurobiology, Institute of Neurosciences, IdISSC, Hospital Clínico San Carlos, Universidad Complutense de Madrid, Madrid, Spain.
| | - L Torre-Fuentes
- Laboratory of Neurobiology, Institute of Neurosciences, IdISSC, Hospital Clínico San Carlos, Universidad Complutense de Madrid, Madrid, Spain
| | - J A Matías-Guiu
- Department of Neurology, Institute of Neurosciences, IdISSC, Hospital Clínico San Carlos, Universidad Complutense de Madrid, Madrid, Spain
| | - V Pytel
- Laboratory of Neurobiology, Institute of Neurosciences, IdISSC, Hospital Clínico San Carlos, Universidad Complutense de Madrid, Madrid, Spain; Department of Neurology, Institute of Neurosciences, IdISSC, Hospital Clínico San Carlos, Universidad Complutense de Madrid, Madrid, Spain
| | - D D Ojeda-Hernández
- Laboratory of Neurobiology, Institute of Neurosciences, IdISSC, Hospital Clínico San Carlos, Universidad Complutense de Madrid, Madrid, Spain
| | - B Selma-Calvo
- Laboratory of Neurobiology, Institute of Neurosciences, IdISSC, Hospital Clínico San Carlos, Universidad Complutense de Madrid, Madrid, Spain
| | - P Montero-Escribano
- Department of Neurology, Institute of Neurosciences, IdISSC, Hospital Clínico San Carlos, Universidad Complutense de Madrid, Madrid, Spain
| | - L Vidorreta-Ballesteros
- Department of Neurology, Institute of Neurosciences, IdISSC, Hospital Clínico San Carlos, Universidad Complutense de Madrid, Madrid, Spain
| | - J Matías-Guiu
- Laboratory of Neurobiology, Institute of Neurosciences, IdISSC, Hospital Clínico San Carlos, Universidad Complutense de Madrid, Madrid, Spain; Department of Neurology, Institute of Neurosciences, IdISSC, Hospital Clínico San Carlos, Universidad Complutense de Madrid, Madrid, Spain
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2
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Gomez-Pinedo U, Matías-Guiu JA, Torre-Fuentes L, Montero-Escribano P, Hernández-Lorenzo L, Pytel V, Maietta P, Alvarez S, Sanclemente-Alamán I, Moreno-Jimenez L, Ojeda-Hernandez D, Villar-Gómez N, Benito-Martin MS, Selma-Calvo B, Vidorreta-Ballesteros L, Madrid R, Matías-Guiu J. Variant rs4149584 (R92Q) of the TNFRSF1A gene in patients with familial multiple sclerosis. Neurologia 2022:S2173-5808(22)00087-6. [PMID: 35963536 DOI: 10.1016/j.nrleng.2022.07.002] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/28/2022] [Accepted: 07/15/2022] [Indexed: 11/26/2022] Open
Abstract
INTRODUCTION Genomic studies have identified numerous genetic variants associated with susceptibility to multiple sclerosis (MS); however, each one explains only a small percentage of the risk of developing the disease. These variants are located in genes involved in specific pathways, which supports the hypothesis that the risk of developing MS may be linked to alterations in these pathways, rather than in specific genes. We analyzed the role of the TNFRSF1A gene, which encodes one of the TNF-α receptors involved in a signaling pathway previously linked to autoimmune disease. METHODS We included 138 individuals from 23 families including at least 2 members with MS, and analyzed the presence of exonic variants of TNFRSF1A through whole-exome sequencing. We also conducted a functional study to analyze the pathogenic mechanism of variant rs4149584 (-g.6442643C > G, NM_001065.4:c.362 G > A, R92Q) by plasmid transfection into human oligodendroglioma (HOG) cells, which behave like oligodendrocyte lineage cells; protein labeling was used to locate the protein within cells. We also analyzed the ability of transfected HOG cells to proliferate and differentiate into oligodendrocytes. RESULTS Variant rs4149584 was found in 2 patients with MS (3.85%), one patient with another autoimmune disease (7.6%), and in 5 unaffected individuals (7.46%). The 2 patients with MS and variant rs4149584 were homozygous carriers and belonged to the same family, whereas the remaining individuals presented the variant in heterozygosis. The study of HOG cells transfected with the mutation showed that the protein does not reach the cell membrane, but rather accumulates in the cytoplasm, particularly in the endoplasmic reticulum and near the nucleus; this suggests that, in the cells presenting the mutation, TNFRSF1 does not act as a transmembrane protein, which may alter its signaling pathway. The study of cell proliferation and differentiation found that transfected cells continue to be able to differentiate into oligodendrocytes and are probably still capable of producing myelin, although they present a lower rate of proliferation than wild-type cells. CONCLUSIONS Variant rs4149584 is associated with risk of developing MS. We analyzed its functional role in oligodendrocyte lineage cells and found an association with MS in homozygous carriers. However, the associated molecular alterations do not influence the differentiation into oligodendrocytes; we were therefore unable to confirm whether this variant alone is pathogenic in MS, at least in heterozygosis.
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Affiliation(s)
- U Gomez-Pinedo
- Laboratory of Neurobiology, Institute of Neurosciences, IdISSC, Hospital Clínico San Carlos, Universidad Complutense de Madrid, Madrid, Spain.
| | - J A Matías-Guiu
- Department of Neurology, Institute of Neurosciences, IdISSC, Hospital Clínico San Carlos, Universidad Complutense de Madrid, Madrid, Spain
| | - L Torre-Fuentes
- Laboratory of Neurobiology, Institute of Neurosciences, IdISSC, Hospital Clínico San Carlos, Universidad Complutense de Madrid, Madrid, Spain
| | - P Montero-Escribano
- Department of Neurology, Institute of Neurosciences, IdISSC, Hospital Clínico San Carlos, Universidad Complutense de Madrid, Madrid, Spain
| | - L Hernández-Lorenzo
- Department of Neurology, Institute of Neurosciences, IdISSC, Hospital Clínico San Carlos, Universidad Complutense de Madrid, Madrid, Spain
| | - V Pytel
- Laboratory of Neurobiology, Institute of Neurosciences, IdISSC, Hospital Clínico San Carlos, Universidad Complutense de Madrid, Madrid, Spain; Department of Neurology, Institute of Neurosciences, IdISSC, Hospital Clínico San Carlos, Universidad Complutense de Madrid, Madrid, Spain
| | | | | | - I Sanclemente-Alamán
- Laboratory of Neurobiology, Institute of Neurosciences, IdISSC, Hospital Clínico San Carlos, Universidad Complutense de Madrid, Madrid, Spain
| | - L Moreno-Jimenez
- Laboratory of Neurobiology, Institute of Neurosciences, IdISSC, Hospital Clínico San Carlos, Universidad Complutense de Madrid, Madrid, Spain
| | - D Ojeda-Hernandez
- Laboratory of Neurobiology, Institute of Neurosciences, IdISSC, Hospital Clínico San Carlos, Universidad Complutense de Madrid, Madrid, Spain
| | - N Villar-Gómez
- Laboratory of Neurobiology, Institute of Neurosciences, IdISSC, Hospital Clínico San Carlos, Universidad Complutense de Madrid, Madrid, Spain
| | - M S Benito-Martin
- Laboratory of Neurobiology, Institute of Neurosciences, IdISSC, Hospital Clínico San Carlos, Universidad Complutense de Madrid, Madrid, Spain
| | - B Selma-Calvo
- Laboratory of Neurobiology, Institute of Neurosciences, IdISSC, Hospital Clínico San Carlos, Universidad Complutense de Madrid, Madrid, Spain
| | - L Vidorreta-Ballesteros
- Department of Neurology, Institute of Neurosciences, IdISSC, Hospital Clínico San Carlos, Universidad Complutense de Madrid, Madrid, Spain
| | | | - J Matías-Guiu
- Laboratory of Neurobiology, Institute of Neurosciences, IdISSC, Hospital Clínico San Carlos, Universidad Complutense de Madrid, Madrid, Spain; Department of Neurology, Institute of Neurosciences, IdISSC, Hospital Clínico San Carlos, Universidad Complutense de Madrid, Madrid, Spain
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Whole-genome sequencing identifies functional noncoding variation in SEMA3C that cosegregates with dyslexia in a multigenerational family. Hum Genet 2021; 140:1183-1200. [PMID: 34076780 PMCID: PMC8263547 DOI: 10.1007/s00439-021-02289-w] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.7] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/16/2020] [Accepted: 04/27/2021] [Indexed: 12/11/2022]
Abstract
Dyslexia is a common heritable developmental disorder involving impaired reading abilities. Its genetic underpinnings are thought to be complex and heterogeneous, involving common and rare genetic variation. Multigenerational families segregating apparent monogenic forms of language-related disorders can provide useful entrypoints into biological pathways. In the present study, we performed a genome-wide linkage scan in a three-generational family in which dyslexia affects 14 of its 30 members and seems to be transmitted with an autosomal dominant pattern of inheritance. We identified a locus on chromosome 7q21.11 which cosegregated with dyslexia status, with the exception of two cases of phenocopy (LOD = 2.83). Whole-genome sequencing of key individuals enabled the assessment of coding and noncoding variation in the family. Two rare single-nucleotide variants (rs144517871 and rs143835534) within the first intron of the SEMA3C gene cosegregated with the 7q21.11 risk haplotype. In silico characterization of these two variants predicted effects on gene regulation, which we functionally validated for rs144517871 in human cell lines using luciferase reporter assays. SEMA3C encodes a secreted protein that acts as a guidance cue in several processes, including cortical neuronal migration and cellular polarization. We hypothesize that these intronic variants could have a cis-regulatory effect on SEMA3C expression, making a contribution to dyslexia susceptibility in this family.
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Chen B, Craiu RV, Sun L. Bayesian model averaging for the X-chromosome inactivation dilemma in genetic association study. Biostatistics 2020; 21:319-335. [PMID: 30247537 DOI: 10.1093/biostatistics/kxy049] [Citation(s) in RCA: 7] [Impact Index Per Article: 1.8] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/24/2017] [Accepted: 06/30/2018] [Indexed: 01/17/2023] Open
Abstract
X-chromosome is often excluded from the so called "whole-genome" association studies due to the differences it exhibits between males and females. One particular analytical challenge is the unknown status of X-inactivation, where one of the two X-chromosome variants in females may be randomly selected to be silenced. In the absence of biological evidence in favor of one specific model, we consider a Bayesian model averaging framework that offers a principled way to account for the inherent model uncertainty, providing model averaging-based posterior density intervals and Bayes factors. We examine the inferential properties of the proposed methods via extensive simulation studies, and we apply the methods to a genetic association study of an intestinal disease occurring in about 20% of cystic fibrosis patients. Compared with the results previously reported assuming the presence of inactivation, we show that the proposed Bayesian methods provide more feature-rich quantities that are useful in practice.
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Affiliation(s)
- Bo Chen
- Department of Statistical Sciences, University of Toronto, Sidney Smith Hall, 100 St. George Street, Toronto, ON, Canada
| | - Radu V Craiu
- Department of Statistical Sciences, University of Toronto, Sidney Smith Hall, 100 St. George Street, Toronto, ON, Canada
| | - Lei Sun
- Department of Statistical Sciences, University of Toronto, Sidney Smith Hall, 100 St. George Street, Toronto, ON, Canada
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5
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Hao M, Zhao X, Xu W. Competing risk modeling and testing for X-chromosome genetic association. Comput Stat Data Anal 2020. [DOI: 10.1016/j.csda.2020.107007] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/12/2022]
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Torre-Fuentes L, Matías-Guiu JA, Pytel V, Montero-Escribano P, Maietta P, Álvarez S, Gómez-Pinedo U, Matías-Guiu J. Variants of genes encoding TNF receptors and ligands and proteins regulating TNF activation in familial multiple sclerosis. CNS Neurosci Ther 2020; 26:1178-1184. [PMID: 32951330 PMCID: PMC7564193 DOI: 10.1111/cns.13456] [Citation(s) in RCA: 3] [Impact Index Per Article: 0.8] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/17/2019] [Revised: 08/25/2020] [Accepted: 08/26/2020] [Indexed: 02/05/2023] Open
Abstract
INTRODUCTION Numerous genetic variants have been associated with susceptibility to multiple sclerosis (MS). Variants located in genes involved in specific pathways, such as those affecting TNF-α, can contribute to the risk of MS. The purpose of this study was to determine whether variants of these genes are associated with greater risk of MS. METHODS We used whole-exome sequencing to study genes coding for TNF-α receptors and ligands, and proteins promoting TNF-α expression in 116 individuals from 19 families including at least two MS patients. We compared patients with MS, patients with other autoimmune diseases, and healthy individuals. RESULTS Greater polymorphism was observed in several genes in families with familial MS compared to the general population; this may reflect greater susceptibility to autoimmune diseases. Pedigree analysis also revealed that LT-α variants rs1041981 and rs2229094 and LT-β variant rs4647197 were associated with MS and that LT-β variant rs4647183 was associated with other autoimmune diseases. The association between autoimmune disease and TNFAIP2 variant rs1132339 is particularly noteworthy, as is the fact that TNFAIP6 variant rs1046668 appears to follow a recessive inheritance pattern. CONCLUSIONS Our findings support the idea that the risk of familial MS is associated with variants of signaling pathways, including those involving TNF-α.
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Affiliation(s)
- Laura Torre-Fuentes
- Laboratory of Neurobiology, Institute of Neurosciences, IdISSC, Hospital Clínico San Carlos, Universidad Complutense de Madrid, Madrid, Spain
| | - Jordi A Matías-Guiu
- Department of Neurology, Institute of Neurosciences, IdISSC, Hospital Clínico San Carlos, Universidad Complutense de Madrid, Madrid, Spain
| | - Vanesa Pytel
- Laboratory of Neurobiology, Institute of Neurosciences, IdISSC, Hospital Clínico San Carlos, Universidad Complutense de Madrid, Madrid, Spain.,Department of Neurology, Institute of Neurosciences, IdISSC, Hospital Clínico San Carlos, Universidad Complutense de Madrid, Madrid, Spain
| | - Paloma Montero-Escribano
- Laboratory of Neurobiology, Institute of Neurosciences, IdISSC, Hospital Clínico San Carlos, Universidad Complutense de Madrid, Madrid, Spain
| | | | | | - Ulises Gómez-Pinedo
- Laboratory of Neurobiology, Institute of Neurosciences, IdISSC, Hospital Clínico San Carlos, Universidad Complutense de Madrid, Madrid, Spain
| | - Jorge Matías-Guiu
- Laboratory of Neurobiology, Institute of Neurosciences, IdISSC, Hospital Clínico San Carlos, Universidad Complutense de Madrid, Madrid, Spain.,Department of Neurology, Institute of Neurosciences, IdISSC, Hospital Clínico San Carlos, Universidad Complutense de Madrid, Madrid, Spain
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7
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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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Han D, Hao M, Qu L, Xu W. A novel model for the X-chromosome inactivation association on survival data. Stat Methods Med Res 2020; 29:1305-1314. [PMID: 31258049 DOI: 10.1177/0962280219859037] [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: 11/17/2022]
Abstract
The X-linked genetic association is overlooked in most of the genetic studies because of the complexity of X-chromosome inactivation process. In fact, the biological process of the gene at the same locus can vary across different subjects. Besides, the skewness of X-chromosome inactivation is inherently subject-specific (even tissue-specific within subjects) and cannot be accurately represented by a population-level parameter. To tackle this issue, a new model is proposed to incorporate the X-linked genetic association into right-censored survival data. The novel model can present that the X-linked genes on different subjects may go through different biological processes via a mixed distribution. Further, a random effect is employed to describe the uncertainty of the biological process for every subject. The proposed method can derive the probability for the escape of X-chromosome inactivation and derive the unbiased estimates of the model parameters. The Legendre-Gauss Quadrature method is used to approximate the integration over the random effect. Finite sample performance of this method is examined via extensive simulation studies. An application is illustrated with the implementation on a cancer genetic study with right-censored survival data.
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Affiliation(s)
- Dongxiao Han
- Academy of Mathematics and Systems Science, Chinese Academy of Sciences, Beijing, China
| | - Meiling Hao
- School of Statistics, University of International Business and Economics, Beijing, China
| | - Lianqiang Qu
- School of Mathematics and Statistics, Central China Normal University, Wuhan, China
| | - Wei Xu
- Department of Biostatistics, Princess Margaret Cancer Centre, Toronto, Canada; Dalla Lana School of Public Health, University of Toronto, Toronto, Canada
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Pytel V, Matías-Guiu JA, Torre-Fuentes L, Montero-Escribano P, Maietta P, Botet J, Álvarez S, Gómez-Pinedo U, Matías-Guiu J. Exonic variants of genes related to the vitamin D signaling pathway in the families of familial multiple sclerosis using whole-exome next generation sequencing. Brain Behav 2019; 9:e01272. [PMID: 30900415 PMCID: PMC6456803 DOI: 10.1002/brb3.1272] [Citation(s) in RCA: 18] [Impact Index Per Article: 3.6] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 01/03/2019] [Revised: 02/27/2019] [Accepted: 03/06/2019] [Indexed: 12/12/2022] Open
Abstract
INTRODUCTION Vitamin D (VD) deficiency has been associated with multiple sclerosis (MS) and other autoimmune diseases (AIDs). However, the effect of the genetics of VD on the risk of MS is subject to debate. This study focuses on genes linked to the VD signaling pathway in families with MS. The evaluation of gene variants in all the members of families could contribute to an additional knowledge on the information obtained from case-control studies that use nonrelated healthy people. MATERIAL AND METHODS We studied 94 individuals from 15 families including at least two patients with MS. We performed whole-exome next generation sequencing on all individuals and analyzed variants of the DHCR7, CYP2R1, CYP3A4, CYP27A1, GC, CYP27B1, LRP2, CUBN, DAB2, FCGR, RXR, VDR, CYP24A1, and PDIA3 genes. We also studied PTH, FGF23, METTL1, METTL21B, and the role of the linkage disequilibrium block on the long arm of chromosome 12, through analysis of the CDK4, TSFM, AGAP2, and AVIL genes. We compared patients with MS, other AIDs and unaffected members from different family types. RESULTS The study described the variants in the VD signaling pathway that appear in families with at least two patients with MS. Some infrequent variants were detected in these families, but no significant difference was observed between patients with MS and/or other AIDs and unaffected family members in the frequency of these variants. Variants previously associated with MS in the literature were not observed in these families or were distributed similarly in patients and unaffected family members. CONCLUSION The study of genes involved in the VD signaling pathway in families that include more than one patient with MS did not identify any variants that could explain the presence of the disease, suggesting that VD metabolism could probably play a role in MS more as an environmental factor rather than as a genetic factor. Our study also supports the analysis of cases and unaffected individuals within families in order to determine the influence of genetic factors.
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Affiliation(s)
- Vanesa Pytel
- Department of Neurology, Institute of Neurosciences, IdISSC, Hospital Clínico San Carlos, Universidad Complutense de Madrid, Madrid, Spain.,Laboratory of Neurobiology, Institute of Neurosciences, IdISSC, Hospital Clínico San Carlos, Universidad Complutense de Madrid, Madrid, Spain
| | - Jordi A Matías-Guiu
- Department of Neurology, Institute of Neurosciences, IdISSC, Hospital Clínico San Carlos, Universidad Complutense de Madrid, Madrid, Spain
| | - Laura Torre-Fuentes
- Laboratory of Neurobiology, Institute of Neurosciences, IdISSC, Hospital Clínico San Carlos, Universidad Complutense de Madrid, Madrid, Spain
| | - Paloma Montero-Escribano
- Department of Neurology, Institute of Neurosciences, IdISSC, Hospital Clínico San Carlos, Universidad Complutense de Madrid, Madrid, Spain
| | | | | | | | - Ulises Gómez-Pinedo
- Laboratory of Neurobiology, Institute of Neurosciences, IdISSC, Hospital Clínico San Carlos, Universidad Complutense de Madrid, Madrid, Spain
| | - Jorge Matías-Guiu
- Department of Neurology, Institute of Neurosciences, IdISSC, Hospital Clínico San Carlos, Universidad Complutense de Madrid, Madrid, Spain.,Laboratory of Neurobiology, Institute of Neurosciences, IdISSC, Hospital Clínico San Carlos, Universidad Complutense de Madrid, Madrid, Spain
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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: 30] [Impact Index Per Article: 5.0] [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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Blue EE, Yu CE, Thornton TA, Chapman NH, Kernfeld E, Jiang N, Shively KM, Buckingham KJ, Marvin CT, Bamshad MJ, Bird TD, Wijsman EM. Variants regulating ZBTB4 are associated with age-at-onset of Alzheimer's disease. GENES, BRAIN, AND BEHAVIOR 2018; 17:e12429. [PMID: 29045054 PMCID: PMC5902667 DOI: 10.1111/gbb.12429] [Citation(s) in RCA: 6] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 08/04/2017] [Revised: 10/11/2017] [Accepted: 10/12/2017] [Indexed: 01/01/2023]
Abstract
The identification of novel genetic modifiers of age-at-onset (AAO) of Alzheimer's disease (AD) could advance our understanding of AD and provide novel therapeutic targets. A previous genome scan for modifiers of AAO among families affected by early-onset AD caused by the PSEN2 N141I variant identified 2 loci with significant evidence for linkage: 1q23.3 and 17p13.2. Here, we describe the fine-mapping of these 2 linkage regions, and test for replication in 6 independent datasets. By fine-mapping these linkage signals in a single large family, we reduced the linkage regions to 11% their original size and nominated 54 candidate variants. Among the 11 variants associated with AAO of AD in a larger sample of Germans from Russia, the strongest evidence implicated promoter variants influencing NCSTN on 1q23.3 and ZBTB4 on 17p13.2. The association between ZBTB4 and AAO of AD was replicated by multiple variants in independent, trans-ethnic datasets. Our results show association between AAO of AD and both ZBTB4 and NCSTN. ZBTB4 is a transcriptional repressor that regulates the cell cycle, including the apoptotic response to amyloid beta, while NCSTN is part of the gamma secretase complex, known to influence amyloid beta production. These genes therefore suggest important roles for amyloid beta and cell cycle pathways in AAO of AD.
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Affiliation(s)
- Elizabeth E. Blue
- Division of Medical Genetics, University of Washington, Seattle, WA 98195, USA
| | - Chang-En Yu
- Division of Gerontology, University of Washington, Seattle, WA 98195, USA
- Geriatric Research, Education, and Clinical Center, VA Puget Sound Health Care System, Seattle, WA 98108, USA
| | - Timothy A. Thornton
- Department of Biostatistics, University of Washington, Seattle, WA 98195, USA
| | - Nicola H. Chapman
- Division of Medical Genetics, University of Washington, Seattle, WA 98195, USA
| | - Eric Kernfeld
- Department of Biostatistics, University of Washington, Seattle, WA 98195, USA
| | - Nan Jiang
- Department of Biology, University of Washington, Seattle, WA 98195, USA
| | - Kathryn M. Shively
- Department of Pediatrics, University of Washington, Seattle, WA 98195, USA
| | - Kati J. Buckingham
- Department of Pediatrics, University of Washington, Seattle, WA 98195, USA
| | - Colby T. Marvin
- Department of Pediatrics, University of Washington, Seattle, WA 98195, USA
| | - Michael J. Bamshad
- Department of Pediatrics, University of Washington, Seattle, WA 98195, USA
- Department of Genome Sciences, University of Washington, Seattle, WA 98195, USA
- Division of Genetic Medicine, Seattle Children’s Hospital, Seattle, WA 98105, USA
| | - Thomas D. Bird
- Division of Medical Genetics, University of Washington, Seattle, WA 98195, USA
- Geriatric Research, Education, and Clinical Center, VA Puget Sound Health Care System, Seattle, WA 98108, USA
- Department of Neurology, University of Washington, Seattle, WA 98195, USA
| | - Ellen M. Wijsman
- Division of Medical Genetics, University of Washington, Seattle, WA 98195, USA
- Department of Biostatistics, University of Washington, Seattle, WA 98195, USA
- Department of Genome Sciences, University of Washington, Seattle, WA 98195, USA
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12
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A unified partial likelihood approach for X-chromosome association on time-to-event outcomes. Genet Epidemiol 2017; 42:80-94. [DOI: 10.1002/gepi.22097] [Citation(s) in RCA: 11] [Impact Index Per Article: 1.6] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/08/2017] [Revised: 09/20/2017] [Accepted: 10/10/2017] [Indexed: 01/23/2023]
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13
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Choi S, Lee S, Qiao D, Hardin M, Cho MH, Silverman EK, Park T, Won S. FARVATX: Family-Based Rare Variant Association Test for X-Linked Genes. Genet Epidemiol 2016; 40:475-85. [PMID: 27325607 DOI: 10.1002/gepi.21979] [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: 07/30/2015] [Revised: 03/05/2016] [Accepted: 04/04/2016] [Indexed: 11/06/2022]
Abstract
Although the X chromosome has many genes that are functionally related to human diseases, the complicated biological properties of the X chromosome have prevented efficient genetic association analyses, and only a few significantly associated X-linked variants have been reported for complex traits. For instance, dosage compensation of X-linked genes is often achieved via the inactivation of one allele in each X-linked variant in females; however, some X-linked variants can escape this X chromosome inactivation. Efficient genetic analyses cannot be conducted without prior knowledge about the gene expression process of X-linked variants, and misspecified information can lead to power loss. In this report, we propose new statistical methods for rare X-linked variant genetic association analysis of dichotomous phenotypes with family-based samples. The proposed methods are computationally efficient and can complete X-linked analyses within a few hours. Simulation studies demonstrate the statistical efficiency of the proposed methods, which were then applied to rare-variant association analysis of the X chromosome in chronic obstructive pulmonary disease. Some promising significant X-linked genes were identified, illustrating the practical importance of the proposed methods.
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Affiliation(s)
- Sungkyoung Choi
- Interdisciplinary Program in Bioinformatics, Seoul National University, Seoul, Korea
| | - Sungyoung Lee
- Interdisciplinary Program in Bioinformatics, Seoul National University, Seoul, Korea
| | - Dandi Qiao
- Channing Division of Network Medicine, Brigham and Women's Hospital, Harvard Medical School, Boston, Massachusetts, United States of America
| | - Megan Hardin
- Channing Division of Network Medicine, Brigham and Women's Hospital, Harvard Medical School, Boston, Massachusetts, United States of America.,Division of Pulmonary and Critical Care Medicine, Brigham and Women's Hospital, Boston, Massachusetts, United States of America
| | - Michael H Cho
- Channing Division of Network Medicine, Brigham and Women's Hospital, Harvard Medical School, Boston, Massachusetts, United States of America.,Division of Pulmonary and Critical Care Medicine, Brigham and Women's Hospital, Boston, Massachusetts, United States of America
| | - Edwin K Silverman
- Channing Division of Network Medicine, Brigham and Women's Hospital, Harvard Medical School, Boston, Massachusetts, United States of America.,Division of Pulmonary and Critical Care Medicine, Brigham and Women's Hospital, Boston, Massachusetts, United States of America
| | - Taesung Park
- Interdisciplinary Program in Bioinformatics, Seoul National University, Seoul, Korea.,Department of Statistics, Seoul National University, Seoul, Korea
| | - Sungho Won
- Interdisciplinary Program in Bioinformatics, Seoul National University, Seoul, Korea.,Department of Public Health Science, Seoul National University, Seoul, Korea.,Institute of Health and Environment, Seoul National University, Seoul, Korea
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14
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Rajendran B, Janakarajan VN. Circadian clock gene aryl hydrocarbon receptor nuclear translocator-like polymorphisms are associated with seasonal affective disorder: An Indian family study. Indian J Psychiatry 2016; 58:57-60. [PMID: 26985106 PMCID: PMC4776583 DOI: 10.4103/0019-5545.174374] [Citation(s) in RCA: 9] [Impact Index Per Article: 1.1] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Indexed: 12/11/2022] Open
Abstract
BACKGROUND AND AIM Polymorphisms in aryl hydrocarbon receptor nuclear translocator-like (ARNTL) gene, the key component of circadian clock manifests circadian rhythm abnormalities. As seasonal affective disorder (SAD) is associated with disrupted circadian rhythms, the main objective of this study was to screen an Indian family with SAD for ARNTL gene polymorphisms. MATERIALS AND METHODS In this study, 30 members of close-knit family with SAD, 30 age- and sex-matched controls of the same caste with no prior history of psychiatric illness and 30 age- and sex-matched controls belonging to 17 different castes with no prior history of psychiatric illness were genotyped for five different single nucleotide polymorphisms (SNPs) in ARNTL gene by TaqMan allele-specific genotyping assay. STATISTICAL ANALYSIS Statistical significance was assessed by more powerful quasi-likelihood score test-XM. RESULTS Most of the family members carried the risk alleles and we observed a highly significant SNP rs2279287 (A/G) in ARNTL gene with an allelic frequency of 0.75. CONCLUSIONS Polymorphisms in ARNTL gene disrupt circadian rhythms causing SAD and genetic predisposition becomes more deleterious in the presence of adverse environment.
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Affiliation(s)
- Bhagya Rajendran
- Research and Development Centre, Bharathiar University, Coimbatore, Tamil Nadu, India
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15
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Winham SJ, Jenkins GD, Biernacka JM. Modeling X Chromosome Data Using Random Forests: Conquering Sex Bias. Genet Epidemiol 2015; 40:123-32. [PMID: 26639183 DOI: 10.1002/gepi.21946] [Citation(s) in RCA: 5] [Impact Index Per Article: 0.6] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/22/2015] [Revised: 10/29/2015] [Accepted: 10/29/2015] [Indexed: 12/12/2022]
Abstract
Machine learning methods, including Random Forests (RF), are increasingly used for genetic data analysis. However, the standard RF algorithm does not correctly model the effects of X chromosome single nucleotide polymorphisms (SNPs), leading to biased estimates of variable importance. We propose extensions of RF to correctly model X SNPs, including a stratified approach and an approach based on the process of X chromosome inactivation. We applied the new and standard RF approaches to case-control alcohol dependence data from the Study of Addiction: Genes and Environment (SAGE), and compared the performance of the alternative approaches via a simulation study. Standard RF applied to a case-control study of alcohol dependence yielded inflated variable importance estimates for X SNPs, even when sex was included as a variable, but the results of the new RF methods were consistent with univariate regression-based approaches that correctly model X chromosome data. Simulations showed that the new RF methods eliminate the bias in standard RF variable importance for X SNPs when sex is associated with the trait, and are able to detect causal autosomal and X SNPs. Even in the absence of sex effects, the new extensions perform similarly to standard RF. Thus, we provide a powerful multimarker approach for genetic analysis that accommodates X chromosome data in an unbiased way. This method is implemented in the freely available R package "snpRF" (http://www.cran.r-project.org/web/packages/snpRF/).
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Affiliation(s)
- Stacey J Winham
- Department of Health Sciences Research, Mayo Clinic, Rochester, Minnesota, United States of America
| | - Gregory D Jenkins
- Department of Health Sciences Research, Mayo Clinic, Rochester, Minnesota, United States of America
| | - Joanna M Biernacka
- Department of Health Sciences Research, Mayo Clinic, Rochester, Minnesota, United States of America.,Department of Psychiatry and Psychology, Mayo Clinic, Rochester, Minnesota, United States of America
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16
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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: 9.8] [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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17
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Villanueva P, Nudel R, Hoischen A, Fernández MA, Simpson NH, Gilissen C, Reader RH, Jara L, Echeverry MM, Francks C, Baird G, Conti-Ramsden G, O’Hare A, Bolton PF, Hennessy ER, Palomino H, Carvajal-Carmona L, Veltman JA, Cazier JB, De Barbieri Z, Fisher SE, Newbury DF. Exome sequencing in an admixed isolated population indicates NFXL1 variants confer a risk for specific language impairment. PLoS Genet 2015; 11:e1004925. [PMID: 25781923 PMCID: PMC4363375 DOI: 10.1371/journal.pgen.1004925] [Citation(s) in RCA: 33] [Impact Index Per Article: 3.7] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/03/2014] [Accepted: 11/25/2014] [Indexed: 11/06/2022] Open
Abstract
Children affected by Specific Language Impairment (SLI) fail to acquire age appropriate language skills despite adequate intelligence and opportunity. SLI is highly heritable, but the understanding of underlying genetic mechanisms has proved challenging. In this study, we use molecular genetic techniques to investigate an admixed isolated founder population from the Robinson Crusoe Island (Chile), who are affected by a high incidence of SLI, increasing the power to discover contributory genetic factors. We utilize exome sequencing in selected individuals from this population to identify eight coding variants that are of putative significance. We then apply association analyses across the wider population to highlight a single rare coding variant (rs144169475, Minor Allele Frequency of 4.1% in admixed South American populations) in the NFXL1 gene that confers a nonsynonymous change (N150K) and is significantly associated with language impairment in the Robinson Crusoe population (p = 2.04 × 10-4, 8 variants tested). Subsequent sequencing of NFXL1 in 117 UK SLI cases identified four individuals with heterozygous variants predicted to be of functional consequence. We conclude that coding variants within NFXL1 confer an increased risk of SLI within a complex genetic model.
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Affiliation(s)
- Pía Villanueva
- Human Genetics Program, Institute of Biomedical Sciences (ICBM), Faculty of Medicine, University of Chile, Santiago, Chile
- School of Speech and Hearing Therapy, Faculty of Medicine, University of Chile, Santiago, Chile
- Department of Child and Dental Maxillary Orthopedics, Faculty of Dentistry, University of Chile, Santiago, Chile
- Doctoral Program of Psychology, Graduate School, University of Granada, Granada, Spain
| | - Ron Nudel
- Wellcome Trust Centre for Human Genetics, University of Oxford, Oxford, United Kingdom
| | - Alexander Hoischen
- Department of Human Genetics, Radboud Institute for Molecular Life Sciences and Donders Centre for Neuroscience, Radboud University Medical Center, Nijmegen, the Netherlands
| | | | - Nuala H. Simpson
- Wellcome Trust Centre for Human Genetics, University of Oxford, Oxford, United Kingdom
| | - Christian Gilissen
- Department of Human Genetics, Radboud Institute for Molecular Life Sciences and Donders Centre for Neuroscience, Radboud University Medical Center, Nijmegen, the Netherlands
| | - Rose H. Reader
- Wellcome Trust Centre for Human Genetics, University of Oxford, Oxford, United Kingdom
| | - Lillian Jara
- Human Genetics Program, Institute of Biomedical Sciences (ICBM), Faculty of Medicine, University of Chile, Santiago, Chile
| | - Maria Magdalena Echeverry
- Grupo de Citogenetica, Filogenia y Evolucion de las Poblaciones, Facultades de Ciencias y de Ciencias de la Salud, Universidad del Tolima, Ibague, Colombia
| | - Clyde Francks
- Max Planck Institute for Psycholinguistics, Nijmegen, the Netherlands
- Donders Institute for Brain, Cognition and Behaviour, Radboud University, Nijmegen, the Netherlands
| | - Gillian Baird
- Newcomen Centre, the Evelina Children’s Hospital, London, United Kingdom
| | - Gina Conti-Ramsden
- School of Psychological Sciences, University of Manchester, Manchester, United Kingdom
| | - Anne O’Hare
- Department of Reproductive and Developmental Sciences, University of Edinburgh, Edinburgh, United Kingdom
| | - Patrick F. Bolton
- Departments of Child & Adolescent Psychiatry & Social Genetic & Developmental Psychiatry Centre, Institute of Psychiatry, King’s College London, London, United Kingdom
| | | | | | - Hernán Palomino
- Department of Child and Dental Maxillary Orthopedics, Faculty of Dentistry, University of Chile, Santiago, Chile
| | - Luis Carvajal-Carmona
- Grupo de Citogenetica, Filogenia y Evolucion de las Poblaciones, Facultades de Ciencias y de Ciencias de la Salud, Universidad del Tolima, Ibague, Colombia
- UC Davis Genome Center, Department of Biochemistry and Molecular Medicine, School of Medicine, University of California Davis, Davis, California, United States of America
| | - Joris A. Veltman
- Department of Human Genetics, Radboud Institute for Molecular Life Sciences and Donders Centre for Neuroscience, Radboud University Medical Center, Nijmegen, the Netherlands
| | - Jean-Baptiste Cazier
- Department of Oncology, University of Oxford, Oxford, United Kingdom
- Centre for Computational Biology, University of Birmingham, Edgbaston, United Kingdom
| | - Zulema De Barbieri
- School of Speech and Hearing Therapy, Faculty of Medicine, University of Chile, Santiago, Chile
| | - Simon E. Fisher
- Max Planck Institute for Psycholinguistics, Nijmegen, the Netherlands
- Donders Institute for Brain, Cognition and Behaviour, Radboud University, Nijmegen, the Netherlands
| | - Dianne F. Newbury
- Wellcome Trust Centre for Human Genetics, University of Oxford, Oxford, United Kingdom
- St Johns College, University of Oxford, Oxford, United Kingdom
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18
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Hong X, Hao K, Ladd-Acosta C, Hansen KD, Tsai HJ, Liu X, Xu X, Thornton TA, Caruso D, Keet CA, Sun Y, Wang G, Luo W, Kumar R, Fuleihan R, Singh AM, Kim JS, Story RE, Gupta RS, Gao P, Chen Z, Walker SO, Bartell TR, Beaty TH, Fallin MD, Schleimer R, Holt PG, Nadeau KC, Wood RA, Pongracic JA, Weeks DE, Wang X. Genome-wide association study identifies peanut allergy-specific loci and evidence of epigenetic mediation in US children. Nat Commun 2015; 6:6304. [PMID: 25710614 PMCID: PMC4340086 DOI: 10.1038/ncomms7304] [Citation(s) in RCA: 157] [Impact Index Per Article: 17.4] [Reference Citation Analysis] [Abstract] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/28/2014] [Accepted: 01/16/2015] [Indexed: 12/17/2022] Open
Abstract
Food allergy (FA) affects 2–10% of U.S. children and is a growing clinical and public health problem. Here we conduct the first genome-wide association study of well-defined FA, including specific subtypes (peanut, milk, and egg) in 2,759 U.S. participants (1,315 children; 1,444 parents) from the Chicago Food Allergy Study; and identify peanut allergy (PA)-specific loci in the HLA-DR and -DQ gene region at 6p21.32, tagged by rs7192 (p=5.5×10−8) and rs9275596 (p=6.8×10−10), in 2,197 participants of European ancestry. We replicate these associations in an independent sample of European ancestry. These associations are further supported by meta-analyses across the discovery and replication samples. Both single-nucleotide polymorphisms (SNPs) are associated with differential DNA methylation levels at multiple CpG sites (p<5×10−8); and differential DNA methylation of the HLA-DQB1 and HLA-DRB1 genes partially mediate the identified SNP-PA associations. This study suggests that the HLA-DR and -DQ gene region likely poses significant genetic risk for PA.
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Affiliation(s)
- Xiumei Hong
- Department of Population, Family and Reproductive Health, Center on the Early Life Origins of Disease, Johns Hopkins University Bloomberg School of Public Health, 615 North Wolfe Street, E4132, Baltimore, Maryland 21205, USA
| | - Ke Hao
- Department of Genetics and Genomic Sciences, Icahn School of Medicine at Mount Sinai, New York, New York 10029, USA
| | - Christine Ladd-Acosta
- Department of Epidemiology, Johns Hopkins University Bloomberg School of Public Health, Baltimore, Maryland 21205, USA
| | - Kasper D Hansen
- 1] Department of Biostatistics, Johns Hopkins University Bloomberg School of Public Health Baltimore, Baltimore, Maryland 21205, USA [2] McKusick-Nathans Insitute of Genetic Medicine, Johns Hopkins University School of Medicine, Baltimore, Maryland 21205, USA
| | - Hui-Ju Tsai
- 1] Mary Ann and J. Milburn Smith Child Health Research Program, Department of Pediatrics, Northwestern University Feinberg School of Medicine and Stanley Manne Children's Research Institute, Ann and Robert H. Lurie Children's Hospital of Chicago, Chicago, Illinois 60611, USA [2] Division of Biostatistics and Bioinformatics, Institute of Population Health Sciences, National Health Research Institutes, Zhunan 35053, Taiwan [3] Department of Public Health, China Medical University, Taichung 40402, Taiwan
| | - Xin Liu
- 1] Mary Ann and J. Milburn Smith Child Health Research Program, Department of Pediatrics, Northwestern University Feinberg School of Medicine and Stanley Manne Children's Research Institute, Ann and Robert H. Lurie Children's Hospital of Chicago, Chicago, Illinois 60611, USA [2] Department of Preventive Medicine, Northwestern University Feinberg School of Medicine, Chicago, Illinois 60611, USA
| | - Xin Xu
- Guangdong Provincial Institute of Nephrology, Southern Medical University, Guangzhou 510515, China
| | - Timothy A Thornton
- Department of Biostatistics, University of Washington, Seattle, Washington 98195, USA
| | - Deanna Caruso
- Department of Population, Family and Reproductive Health, Center on the Early Life Origins of Disease, Johns Hopkins University Bloomberg School of Public Health, 615 North Wolfe Street, E4132, Baltimore, Maryland 21205, USA
| | - Corinne A Keet
- 1] Department of Epidemiology, Johns Hopkins University Bloomberg School of Public Health, Baltimore, Maryland 21205, USA [2] Division of Pediatric Allergy and Immunology, Johns Hopkins University School of Medicine, Baltimore, Maryland 21287, USA
| | - Yifei Sun
- Department of Biostatistics, Johns Hopkins University Bloomberg School of Public Health Baltimore, Baltimore, Maryland 21205, USA
| | - Guoying Wang
- Department of Population, Family and Reproductive Health, Center on the Early Life Origins of Disease, Johns Hopkins University Bloomberg School of Public Health, 615 North Wolfe Street, E4132, Baltimore, Maryland 21205, USA
| | - Wei Luo
- 1] Department of Genetics and Genomic Sciences, Icahn School of Medicine at Mount Sinai, New York, New York 10029, USA [2] College of Computer Science and Technology, Huaqiao University, Xiamen 361021, China
| | - Rajesh Kumar
- Division of Allergy and Immunology, Ann and Robert H. Lurie Children's Hospital of Chicago, Chicago, Illinois 60611, USA
| | - Ramsay Fuleihan
- Division of Allergy and Immunology, Ann and Robert H. Lurie Children's Hospital of Chicago, Chicago, Illinois 60611, USA
| | - Anne Marie Singh
- Department of Pediatrics and Medicine, Ann and Robert H. Lurie Children's Hospital of Chicago, Northwestern Feinberg School of Medicine, Chicago, Illinois 61611, USA
| | - Jennifer S Kim
- 1] Division of Allergy and Immunology, Ann and Robert H. Lurie Children's Hospital of Chicago, Chicago, Illinois 60611, USA [2] NorthShore University HealthSystem, Evanston, Illinois 60201, USA
| | - Rachel E Story
- 1] Division of Allergy and Immunology, Ann and Robert H. Lurie Children's Hospital of Chicago, Chicago, Illinois 60611, USA [2] NorthShore University Health Systems, Pritzker School of Medicine, University of Chicago, Chicago, Illinois 60637, USA
| | - Ruchi S Gupta
- Mary Ann and J. Milburn Smith Child Health Research Program, Department of Pediatrics, Northwestern University Feinberg School of Medicine and Stanley Manne Children's Research Institute, Ann and Robert H. Lurie Children's Hospital of Chicago, Chicago, Illinois 60611, USA
| | - Peisong Gao
- Division of Allergy and Clinical Immunology, Johns Hopkins University School of Medicine, Baltimore, Maryland 21224, USA
| | - Zhu Chen
- Department of Population, Family and Reproductive Health, Center on the Early Life Origins of Disease, Johns Hopkins University Bloomberg School of Public Health, 615 North Wolfe Street, E4132, Baltimore, Maryland 21205, USA
| | - Sheila O Walker
- Department of Population, Family and Reproductive Health, Center on the Early Life Origins of Disease, Johns Hopkins University Bloomberg School of Public Health, 615 North Wolfe Street, E4132, Baltimore, Maryland 21205, USA
| | - Tami R Bartell
- Mary Ann and J. Milburn Smith Child Health Research Program, Department of Pediatrics, Northwestern University Feinberg School of Medicine and Stanley Manne Children's Research Institute, Ann and Robert H. Lurie Children's Hospital of Chicago, Chicago, Illinois 60611, USA
| | - Terri H Beaty
- Department of Epidemiology, Johns Hopkins University Bloomberg School of Public Health, Baltimore, Maryland 21205, USA
| | - M Daniele Fallin
- Department of Mental Health, Wendy Klag Center for Autism and Developmental Disabilities, Bloomberg School of Public Health, Johns Hopkins University, Baltimore, Maryland 21205, USA
| | - Robert Schleimer
- Division of Allergy-Immunology, Northwestern University Feinberg School of Medicine, Chicago, Illinois 60611, USA
| | - Patrick G Holt
- Telethon Kids Institute, University of Western Australia; Perth and Queensland Children's Medical Research Institute, University of Queensland, Brisbane, Queensland 4029, Australia
| | - Kari Christine Nadeau
- Division of Allergy, Immunology and Rheumatology, Stanford University School of Medicine, Stanford, California 94305, USA
| | - Robert A Wood
- Division of Pediatric Allergy and Immunology, Johns Hopkins University School of Medicine, Baltimore, Maryland 21287, USA
| | - Jacqueline A Pongracic
- Division of Allergy and Immunology, Ann and Robert H. Lurie Children's Hospital of Chicago, Chicago, Illinois 60611, USA
| | - Daniel E Weeks
- Departments of Human Genetics and Biostatistics, Graduate School of Public Health, University of Pittsburgh, Pittsburgh, Pennsylvania 15261, USA
| | - Xiaobin Wang
- 1] Department of Population, Family and Reproductive Health, Center on the Early Life Origins of Disease, Johns Hopkins University Bloomberg School of Public Health, 615 North Wolfe Street, E4132, Baltimore, Maryland 21205, USA [2] Division of General Pediatrics and Adolescent Medicine, Department of Pediatrics, Johns Hopkins University School of Medicine, Baltimore, Maryland 21287, USA
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Thornton T. Statistical methods for genome-wide and sequencing association studies of complex traits in related samples. CURRENT PROTOCOLS IN HUMAN GENETICS 2015; 84:1.28.1-1.28.9. [PMID: 25599666 PMCID: PMC4327940 DOI: 10.1002/0471142905.hg0128s84] [Citation(s) in RCA: 10] [Impact Index Per Article: 1.1] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 12/23/2022]
Abstract
Genome-wide association studies (GWAS) and sequencing studies are routinely conducted for the identification of genetic variants that are associated with complex traits. Many genetic studies for association mapping include related individuals. When relatives are included in an association analysis, familial correlations must be appropriately taken into account to ensure correct type I error and to increase power. This unit provides an overview of statistical methods that are available for GWAS and sequencing association studies of complex traits in samples with related individuals.
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Affiliation(s)
- Timothy Thornton
- Department of Biostatistics, University of Washington, Seattle, WA 98195, USA
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20
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Chang D, Gao F, Slavney A, Ma L, Waldman YY, Sams AJ, Billing-Ross P, Madar A, Spritz R, Keinan A. Accounting for eXentricities: analysis of the X chromosome in GWAS reveals X-linked genes implicated in autoimmune diseases. PLoS One 2014; 9:e113684. [PMID: 25479423 PMCID: PMC4257614 DOI: 10.1371/journal.pone.0113684] [Citation(s) in RCA: 84] [Impact Index Per Article: 8.4] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/14/2014] [Accepted: 10/30/2014] [Indexed: 12/12/2022] Open
Abstract
Many complex human diseases are highly sexually dimorphic, suggesting a potential contribution of the X chromosome to disease risk. However, the X chromosome has been neglected or incorrectly analyzed in most genome-wide association studies (GWAS). We present tailored analytical methods and software that facilitate X-wide association studies (XWAS), which we further applied to reanalyze data from 16 GWAS of different autoimmune and related diseases (AID). We associated several X-linked genes with disease risk, among which (1) ARHGEF6 is associated with Crohn's disease and replicated in a study of ulcerative colitis, another inflammatory bowel disease (IBD). Indeed, ARHGEF6 interacts with a gastric bacterium that has been implicated in IBD. (2) CENPI is associated with three different AID, which is compelling in light of known associations with AID of autosomal genes encoding centromere proteins, as well as established autosomal evidence of pleiotropy between autoimmune diseases. (3) We replicated a previous association of FOXP3, a transcription factor that regulates T-cell development and function, with vitiligo; and (4) we discovered that C1GALT1C1 exhibits sex-specific effect on disease risk in both IBDs. These and other X-linked genes that we associated with AID tend to be highly expressed in tissues related to immune response, participate in major immune pathways, and display differential gene expression between males and females. Combined, the results demonstrate the importance of the X chromosome in autoimmunity, reveal the potential of extensive XWAS, even based on existing data, and provide the tools and incentive to properly include the X chromosome in future studies.
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Affiliation(s)
- Diana Chang
- Department of Biological Statistics and Computational Biology, Cornell University, Ithaca, New York, United States of America
- Program in Computational Biology and Medicine, Cornell University, Ithaca, New York, United States of America
| | - Feng Gao
- Department of Biological Statistics and Computational Biology, Cornell University, Ithaca, New York, United States of America
| | - Andrea Slavney
- Department of Biological Statistics and Computational Biology, Cornell University, Ithaca, New York, United States of America
- Graduate Field of Genetics, Genomics and Development, Cornell University, Ithaca, New York, United States of America
| | - Li Ma
- Department of Biological Statistics and Computational Biology, Cornell University, Ithaca, New York, United States of America
- Department of Animal and Avian Sciences, University of Maryland, College Park, Maryland, United States of America
| | - Yedael Y. Waldman
- Department of Biological Statistics and Computational Biology, Cornell University, Ithaca, New York, United States of America
| | - Aaron J. Sams
- Department of Biological Statistics and Computational Biology, Cornell University, Ithaca, New York, United States of America
| | - Paul Billing-Ross
- Department of Biological Statistics and Computational Biology, Cornell University, Ithaca, New York, United States of America
- Graduate Field of Genetics, Genomics and Development, Cornell University, Ithaca, New York, United States of America
| | - Aviv Madar
- Department of Biological Statistics and Computational Biology, Cornell University, Ithaca, New York, United States of America
| | - Richard Spritz
- Human Medical Genetics and Genomics Program, University of Colorado School of Medicine, Aurora, Colorado, United States of America
| | - Alon Keinan
- Department of Biological Statistics and Computational Biology, Cornell University, Ithaca, New York, United States of America
- Program in Computational Biology and Medicine, Cornell University, Ithaca, New York, United States of America
- Graduate Field of Genetics, Genomics and Development, Cornell University, Ithaca, New York, United States of America
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21
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Choi S, Lee S, Cichon S, Nöthen MM, Lange C, Park T, Won S. FARVAT: a family-based rare variant association test. ACTA ACUST UNITED AC 2014; 30:3197-205. [PMID: 25075118 DOI: 10.1093/bioinformatics/btu496] [Citation(s) in RCA: 24] [Impact Index Per Article: 2.4] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/09/2023]
Abstract
MOTIVATION Individuals in each family are genetically more homogeneous than unrelated individuals, and family-based designs are often recommended for the analysis of rare variants. However, despite the importance of family-based samples analysis, few statistical methods for rare variant association analysis are available. RESULTS In this report, we propose a FAmily-based Rare Variant Association Test (FARVAT). FARVAT is based on the quasi-likelihood of whole families, and is statistically and computationally efficient for the extended families. FARVAT assumed that families were ascertained with the disease status of family members, and incorporation of the estimated genetic relationship matrix to the proposed method provided robustness under the presence of the population substructure. Depending on the choice of working matrix, our method could be a burden test or a variance component test, and could be extended to the SKAT-O-type statistic. FARVAT was implemented in C++, and application of the proposed method to schizophrenia data and simulated data for GAW17 illustrated its practical importance. AVAILABILITY The software calculates various statistics for the analysis of related samples, and it is freely downloadable from http://healthstats.snu.ac.kr/software/farvat. CONTACT won1@snu.ac.kr or tspark@stats.snu.ac.kr SUPPLEMENTARY INFORMATION supplementary data are available at Bioinformatics online.
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Affiliation(s)
- Sungkyoung Choi
- Interdisciplinary Program in bioinformatics, Seoul National University, 1 Kwanak-ro Kwanak-gu, Seoul 151-742, Korea, Institute of Human Genetics, University of Bonn, D-53127 Bonn, Germany, Department of Biostatistics, Harvard School of Public Health, 677 Huntington Avenue. Boston, MA 02115, USA, Harvard Medical School, 25 Shattuck St, Boston, MA 02115, USA, Center for Genomic Medicine, Brigham and Women's Hospital, 75 Francis Street, Boston MA 02115, USA, Department of Biostatistics, Harvard School of Public Health, 667 Huntington Ave, Boston, MA 02115, USA, Institute for Genomic Mathematics, University of Bonn, D-53127 Bonn, Germany, German Center for Neurodegenerative Diseases, D-53127 Bonn, Germany, Department of Statistics, Seoul National University 1 Kwanak-ro Kwanak-gu, Seoul 151-742, Korea and Department of Public Health Science, Seoul National University, 1 Kwanak-ro Kwanak-gu, Seoul 151-742, Korea
| | - Sungyoung Lee
- Interdisciplinary Program in bioinformatics, Seoul National University, 1 Kwanak-ro Kwanak-gu, Seoul 151-742, Korea, Institute of Human Genetics, University of Bonn, D-53127 Bonn, Germany, Department of Biostatistics, Harvard School of Public Health, 677 Huntington Avenue. Boston, MA 02115, USA, Harvard Medical School, 25 Shattuck St, Boston, MA 02115, USA, Center for Genomic Medicine, Brigham and Women's Hospital, 75 Francis Street, Boston MA 02115, USA, Department of Biostatistics, Harvard School of Public Health, 667 Huntington Ave, Boston, MA 02115, USA, Institute for Genomic Mathematics, University of Bonn, D-53127 Bonn, Germany, German Center for Neurodegenerative Diseases, D-53127 Bonn, Germany, Department of Statistics, Seoul National University 1 Kwanak-ro Kwanak-gu, Seoul 151-742, Korea and Department of Public Health Science, Seoul National University, 1 Kwanak-ro Kwanak-gu, Seoul 151-742, Korea
| | - Sven Cichon
- Interdisciplinary Program in bioinformatics, Seoul National University, 1 Kwanak-ro Kwanak-gu, Seoul 151-742, Korea, Institute of Human Genetics, University of Bonn, D-53127 Bonn, Germany, Department of Biostatistics, Harvard School of Public Health, 677 Huntington Avenue. Boston, MA 02115, USA, Harvard Medical School, 25 Shattuck St, Boston, MA 02115, USA, Center for Genomic Medicine, Brigham and Women's Hospital, 75 Francis Street, Boston MA 02115, USA, Department of Biostatistics, Harvard School of Public Health, 667 Huntington Ave, Boston, MA 02115, USA, Institute for Genomic Mathematics, University of Bonn, D-53127 Bonn, Germany, German Center for Neurodegenerative Diseases, D-53127 Bonn, Germany, Department of Statistics, Seoul National University 1 Kwanak-ro Kwanak-gu, Seoul 151-742, Korea and Department of Public Health Science, Seoul National University, 1 Kwanak-ro Kwanak-gu, Seoul 151-742, Korea
| | - Markus M Nöthen
- Interdisciplinary Program in bioinformatics, Seoul National University, 1 Kwanak-ro Kwanak-gu, Seoul 151-742, Korea, Institute of Human Genetics, University of Bonn, D-53127 Bonn, Germany, Department of Biostatistics, Harvard School of Public Health, 677 Huntington Avenue. Boston, MA 02115, USA, Harvard Medical School, 25 Shattuck St, Boston, MA 02115, USA, Center for Genomic Medicine, Brigham and Women's Hospital, 75 Francis Street, Boston MA 02115, USA, Department of Biostatistics, Harvard School of Public Health, 667 Huntington Ave, Boston, MA 02115, USA, Institute for Genomic Mathematics, University of Bonn, D-53127 Bonn, Germany, German Center for Neurodegenerative Diseases, D-53127 Bonn, Germany, Department of Statistics, Seoul National University 1 Kwanak-ro Kwanak-gu, Seoul 151-742, Korea and Department of Public Health Science, Seoul National University, 1 Kwanak-ro Kwanak-gu, Seoul 151-742, Korea
| | - Christoph Lange
- Interdisciplinary Program in bioinformatics, Seoul National University, 1 Kwanak-ro Kwanak-gu, Seoul 151-742, Korea, Institute of Human Genetics, University of Bonn, D-53127 Bonn, Germany, Department of Biostatistics, Harvard School of Public Health, 677 Huntington Avenue. Boston, MA 02115, USA, Harvard Medical School, 25 Shattuck St, Boston, MA 02115, USA, Center for Genomic Medicine, Brigham and Women's Hospital, 75 Francis Street, Boston MA 02115, USA, Department of Biostatistics, Harvard School of Public Health, 667 Huntington Ave, Boston, MA 02115, USA, Institute for Genomic Mathematics, University of Bonn, D-53127 Bonn, Germany, German Center for Neurodegenerative Diseases, D-53127 Bonn, Germany, Department of Statistics, Seoul National University 1 Kwanak-ro Kwanak-gu, Seoul 151-742, Korea and Department of Public Health Science, Seoul National University, 1 Kwanak-ro Kwanak-gu, Seoul 151-742, Korea
| | - Taesung Park
- Interdisciplinary Program in bioinformatics, Seoul National University, 1 Kwanak-ro Kwanak-gu, Seoul 151-742, Korea, Institute of Human Genetics, University of Bonn, D-53127 Bonn, Germany, Department of Biostatistics, Harvard School of Public Health, 677 Huntington Avenue. Boston, MA 02115, USA, Harvard Medical School, 25 Shattuck St, Boston, MA 02115, USA, Center for Genomic Medicine, Brigham and Women's Hospital, 75 Francis Street, Boston MA 02115, USA, Department of Biostatistics, Harvard School of Public Health, 667 Huntington Ave, Boston, MA 02115, USA, Institute for Genomic Mathematics, University of Bonn, D-53127 Bonn, Germany, German Center for Neurodegenerative Diseases, D-53127 Bonn, Germany, Department of Statistics, Seoul National University 1 Kwanak-ro Kwanak-gu, Seoul 151-742, Korea and Department of Public Health Science, Seoul National University, 1 Kwanak-ro Kwanak-gu, Seoul 151-742, Korea Interdisciplinary Program in bioinformatics, Seoul National University, 1 Kwanak-ro Kwanak-gu, Seoul 151-742, Korea, Institute of Human Genetics, University of Bonn, D-53127 Bonn, Germany, Department of Biostatistics, Harvard School of Public Health, 677 Huntington Avenue. Boston, MA 02115, USA, Harvard Medical School, 25 Shattuck St, Boston, MA 02115, USA, Center for Genomic Medicine, Brigham and Women's Hospital, 75 Francis Street, Boston MA 02115, USA, Department of Biostatistics, Harvard School of Public Health, 667 Huntington Ave, Boston, MA 02115, USA, Institute for Genomic Mathematics, University of Bonn, D-53127 Bonn, Germany, German Center for Neurodegenerative Diseases, D-53127 Bonn, Germany, Department of Statistics, Seoul National University 1 Kwanak-ro Kwanak-gu, Seoul 151-742, Korea and Department of Public Health Science, Seoul National University, 1 Kwanak-ro Kwanak-gu, Seoul 151-742, Korea
| | - Sungho Won
- Interdisciplinary Program in bioinformatics, Seoul National University, 1 Kwanak-ro Kwanak-gu, Seoul 151-742, Korea, Institute of Human Genetics, University of Bonn, D-53127 Bonn, Germany, Department of Biostatistics, Harvard School of Public Health, 677 Huntington Avenue. Boston, MA 02115, USA, Harvard Medical School, 25 Shattuck St, Boston, MA 02115, USA, Center for Genomic Medicine, Brigham and Women's Hospital, 75 Francis Street, Boston MA 02115, USA, Department of Biostatistics, Harvard School of Public Health, 667 Huntington Ave, Boston, MA 02115, USA, Institute for Genomic Mathematics, University of Bonn, D-53127 Bonn, Germany, German Center for Neurodegenerative Diseases, D-53127 Bonn, Germany, Department of Statistics, Seoul National University 1 Kwanak-ro Kwanak-gu, Seoul 151-742, Korea and Department of Public Health Science, Seoul National University, 1 Kwanak-ro Kwanak-gu, Seoul 151-742, Korea
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Schaid DJ, McDonnell SK, Sinnwell JP, Thibodeau SN. Multiple genetic variant association testing by collapsing and kernel methods with pedigree or population structured data. Genet Epidemiol 2013; 37:409-18. [PMID: 23650101 DOI: 10.1002/gepi.21727] [Citation(s) in RCA: 73] [Impact Index Per Article: 6.6] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/17/2013] [Revised: 03/11/2013] [Accepted: 04/01/2013] [Indexed: 11/11/2022]
Abstract
Searching for rare genetic variants associated with complex diseases can be facilitated by enriching for diseased carriers of rare variants by sampling cases from pedigrees enriched for disease, possibly with related or unrelated controls. This strategy, however, complicates analyses because of shared genetic ancestry, as well as linkage disequilibrium among genetic markers. To overcome these problems, we developed broad classes of "burden" statistics and kernel statistics, extending commonly used methods for unrelated case-control data to allow for known pedigree relationships, for autosomes and the X chromosome. Furthermore, by replacing pedigree-based genetic correlation matrices with estimates of genetic relationships based on large-scale genomic data, our methods can be used to account for population-structured data. By simulations, we show that the type I error rates of our developed methods are near the asymptotic nominal levels, allowing rapid computation of P-values. Our simulations also show that a linear weighted kernel statistic is generally more powerful than a weighted "burden" statistic. Because the proposed statistics are rapid to compute, they can be readily used for large-scale screening of the association of genomic sequence data with disease status.
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Affiliation(s)
- Daniel J Schaid
- Department of Health Sciences Research, Division of Biomedical Statistics and Informatics, Mayo Clinic, Rochester, Minnesota 55905, USA.
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23
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Wise A, Gyi L, Manolio T. eXclusion: toward integrating the X chromosome in genome-wide association analyses. Am J Hum Genet 2013; 92:643-7. [PMID: 23643377 DOI: 10.1016/j.ajhg.2013.03.017] [Citation(s) in RCA: 168] [Impact Index Per Article: 15.3] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/07/2013] [Accepted: 03/19/2013] [Indexed: 12/12/2022] Open
Abstract
The X chromosome lags behind autosomal chromosomes in genome-wide association study (GWAS) findings. Indeed, the X chromosome is commonly excluded from GWAS analyses despite being assayed on all current GWAS microarray platforms. This raises the question: why are so few hits reported on the X chromosome? This commentary aims to examine this question through review of the current X chromosome results in the National Human Genome Research Institute Catalog of Published Genome-Wide Association Studies (NHGRI GWAS Catalog). It will also investigate commonly cited reasons for exclusion of the X chromosome from GWAS and review the tools currently available for X chromosome analysis. It will conclude with recommendations for incorporating X chromosome analyses in future studies.
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