Literature DB >> 25043884

X-chromosome genetic association test accounting for X-inactivation, skewed X-inactivation, and escape from X-inactivation.

Jian Wang1, Robert Yu, Sanjay Shete.   

Abstract

X-chromosome inactivation (XCI) is the process in which one of the two copies of the X-chromosome in females is randomly inactivated to achieve the dosage compensation of X-linked genes between males and females. That is, 50% of the cells have one allele inactive and the other 50% of the cells have the other allele inactive. However, studies have shown that skewed or nonrandom XCI is a biological plausibility wherein more than 75% of cells have the same allele inactive. Also, some of the X-chromosome genes escape XCI, i.e., both alleles are active in all cells. Current statistical tests for X-chromosome association studies can either account for random XCI (e.g., Clayton's approach) or escape from XCI (e.g., PLINK software). Because the true XCI process is unknown and differs across different regions on the X-chromosome, we proposed a unified approach of maximizing likelihood ratio over all biological possibilities: random XCI, skewed XCI, and escape from XCI. A permutation-based procedure was developed to assess the significance of the approach. We conducted simulation studies to compare the performance of the proposed approach with Clayton's approach and PLINK regression. The results showed that the proposed approach has higher powers in the scenarios where XCI is skewed while losing some power in scenarios where XCI is random or XCI is escaped, with well-controlled type I errors. We also applied the approach to the X-chromosomal genetic association study of head and neck cancer.
© 2014 WILEY PERIODICALS, INC.

Entities:  

Keywords:  SNP; X-chromosome; X-chromosome inactivation; escape from X-chromosome inactivation; genome-wide association study; likelihood ratio; skewness

Mesh:

Year:  2014        PMID: 25043884      PMCID: PMC4127090          DOI: 10.1002/gepi.21814

Source DB:  PubMed          Journal:  Genet Epidemiol        ISSN: 0741-0395            Impact factor:   2.135


  41 in total

1.  The dynamics of X-inactivation skewing as women age.

Authors:  C Hatakeyama; C L Anderson; C L Beever; M S Peñaherrera; C J Brown; W P Robinson
Journal:  Clin Genet       Date:  2004-10       Impact factor: 4.438

2.  The NCBI dbGaP database of genotypes and phenotypes.

Authors:  Matthew D Mailman; Michael Feolo; Yumi Jin; Masato Kimura; Kimberly Tryka; Rinat Bagoutdinov; Luning Hao; Anne Kiang; Justin Paschall; Lon Phan; Natalia Popova; Stephanie Pretel; Lora Ziyabari; Moira Lee; Yu Shao; Zhen Y Wang; Karl Sirotkin; Minghong Ward; Michael Kholodov; Kerry Zbicz; Jeffrey Beck; Michael Kimelman; Sergey Shevelev; Don Preuss; Eugene Yaschenko; Alan Graeff; James Ostell; Stephen T Sherry
Journal:  Nat Genet       Date:  2007-10       Impact factor: 38.330

3.  Acquired skewing of X-chromosome inactivation patterns in myeloid cells of the elderly suggests stochastic clonal loss with age.

Authors:  R E Gale; A K Fielding; C N Harrison; D C Linch
Journal:  Br J Haematol       Date:  1997-09       Impact factor: 6.998

4.  eXclusion: toward integrating the X chromosome in genome-wide association analyses.

Authors:  Anastasia L Wise; Lin Gyi; Teri A Manolio
Journal:  Am J Hum Genet       Date:  2013-05-02       Impact factor: 11.025

5.  High frequency of skewed X inactivation in young breast cancer patients.

Authors:  M Kristiansen; A Langerød; G P Knudsen; B L Weber; A L Børresen-Dale; K H Orstavik
Journal:  J Med Genet       Date:  2002-01       Impact factor: 6.318

6.  Expression of genes from the human active and inactive X chromosomes.

Authors:  C J Brown; L Carrel; H F Willard
Journal:  Am J Hum Genet       Date:  1997-06       Impact factor: 11.025

7.  X-inactivation profile reveals extensive variability in X-linked gene expression in females.

Authors:  Laura Carrel; Huntington F Willard
Journal:  Nature       Date:  2005-03-17       Impact factor: 49.962

8.  Nonrandom X-inactivation patterns in normal females: lyonization ratios vary with age.

Authors:  L Busque; R Mio; J Mattioli; E Brais; N Blais; Y Lalonde; M Maragh; D G Gilliland
Journal:  Blood       Date:  1996-07-01       Impact factor: 22.113

9.  Genetic variation in PCDH11X is associated with susceptibility to late-onset Alzheimer's disease.

Authors:  Minerva M Carrasquillo; Fanggeng Zou; V Shane Pankratz; Samantha L Wilcox; Li Ma; Louise P Walker; Samuel G Younkin; Curtis S Younkin; Linda H Younkin; Gina D Bisceglio; Nilufer Ertekin-Taner; Julia E Crook; Dennis W Dickson; Ronald C Petersen; Neill R Graff-Radford; Steven G Younkin
Journal:  Nat Genet       Date:  2009-01-11       Impact factor: 38.330

10.  A new model for random X chromosome inactivation.

Authors:  Joshua Starmer; Terry Magnuson
Journal:  Development       Date:  2008-11-26       Impact factor: 6.868

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  29 in total

Review 1.  Sex Differences in Nonalcoholic Fatty Liver Disease: State of the Art and Identification of Research Gaps.

Authors:  Amedeo Lonardo; Fabio Nascimbeni; Stefano Ballestri; DeLisa Fairweather; Sanda Win; Tin A Than; Manal F Abdelmalek; Ayako Suzuki
Journal:  Hepatology       Date:  2019-09-23       Impact factor: 17.425

2.  FARVATX: Family-Based Rare Variant Association Test for X-Linked Genes.

Authors:  Sungkyoung Choi; Sungyoung Lee; Dandi Qiao; Megan Hardin; Michael H Cho; Edwin K Silverman; Taesung Park; Sungho Won
Journal:  Genet Epidemiol       Date:  2016-06-21       Impact factor: 2.135

3.  Genome-wide association study of 40,000 individuals identifies two novel loci associated with bipolar disorder.

Authors:  Liping Hou; Sarah E Bergen; Nirmala Akula; Jie Song; Christina M Hultman; Mikael Landén; Mazda Adli; Martin Alda; Raffaella Ardau; Bárbara Arias; Jean-Michel Aubry; Lena Backlund; Judith A Badner; Thomas B Barrett; Michael Bauer; Bernhard T Baune; Frank Bellivier; Antonio Benabarre; Susanne Bengesser; Wade H Berrettini; Abesh Kumar Bhattacharjee; Joanna M Biernacka; Armin Birner; Cinnamon S Bloss; Clara Brichant-Petitjean; Elise T Bui; William Byerley; Pablo Cervantes; Caterina Chillotti; Sven Cichon; Francesc Colom; William Coryell; David W Craig; Cristiana Cruceanu; Piotr M Czerski; Tony Davis; Alexandre Dayer; Franziska Degenhardt; Maria Del Zompo; J Raymond DePaulo; Howard J Edenberg; Bruno Étain; Peter Falkai; Tatiana Foroud; Andreas J Forstner; Louise Frisén; Mark A Frye; Janice M Fullerton; Sébastien Gard; Julie S Garnham; Elliot S Gershon; Fernando S Goes; Tiffany A Greenwood; Maria Grigoroiu-Serbanescu; Joanna Hauser; Urs Heilbronner; Stefanie Heilmann-Heimbach; Stefan Herms; Maria Hipolito; Shashi Hitturlingappa; Per Hoffmann; Andrea Hofmann; Stephane Jamain; Esther Jiménez; Jean-Pierre Kahn; Layla Kassem; John R Kelsoe; Sarah Kittel-Schneider; Sebastian Kliwicki; Daniel L Koller; Barbara König; Nina Lackner; Gonzalo Laje; Maren Lang; Catharina Lavebratt; William B Lawson; Marion Leboyer; Susan G Leckband; Chunyu Liu; Anna Maaser; Pamela B Mahon; Wolfgang Maier; Mario Maj; Mirko Manchia; Lina Martinsson; Michael J McCarthy; Susan L McElroy; Melvin G McInnis; Rebecca McKinney; Philip B Mitchell; Marina Mitjans; Francis M Mondimore; Palmiero Monteleone; Thomas W Mühleisen; Caroline M Nievergelt; Markus M Nöthen; Tomas Novák; John I Nurnberger; Evaristus A Nwulia; Urban Ösby; Andrea Pfennig; James B Potash; Peter Propping; Andreas Reif; Eva Reininghaus; John Rice; Marcella Rietschel; Guy A Rouleau; Janusz K Rybakowski; Martin Schalling; William A Scheftner; Peter R Schofield; Nicholas J Schork; Thomas G Schulze; Johannes Schumacher; Barbara W Schweizer; Giovanni Severino; Tatyana Shekhtman; Paul D Shilling; Christian Simhandl; Claire M Slaney; Erin N Smith; Alessio Squassina; Thomas Stamm; Pavla Stopkova; Fabian Streit; Jana Strohmaier; Szabolcs Szelinger; Sarah K Tighe; Alfonso Tortorella; Gustavo Turecki; Eduard Vieta; Julia Volkert; Stephanie H Witt; Adam Wright; Peter P Zandi; Peng Zhang; Sebastian Zollner; Francis J McMahon
Journal:  Hum Mol Genet       Date:  2016-06-21       Impact factor: 6.150

4.  X-chromosome genetic association test incorporating X-chromosome inactivation and imprinting effects.

Authors:  Wei Liu; Bei-Qi Wang; Guojun Liu-Fu; Wing Kam Fung; Ji-Yuan Zhou
Journal:  J Genet       Date:  2019-11       Impact factor: 1.166

5.  Robust association tests for quantitative traits on the X chromosome.

Authors:  Zi-Ying Yang; Wei Liu; Yu-Xin Yuan; Yi-Fan Kong; Pei-Zhen Zhao; Wing Kam Fung; Ji-Yuan Zhou
Journal:  Heredity (Edinb)       Date:  2022-09-10       Impact factor: 3.832

6.  BEXCIS: Bayesian methods for estimating the degree of the skewness of X chromosome inactivation.

Authors:  Wen-Yi Yu; Yu Zhang; Meng-Kai Li; Zi-Ying Yang; Wing Kam Fung; Pei-Zhen Zhao; Ji-Yuan Zhou
Journal:  BMC Bioinformatics       Date:  2022-05-24       Impact factor: 3.307

7.  Modeling X Chromosome Data Using Random Forests: Conquering Sex Bias.

Authors:  Stacey J Winham; Gregory D Jenkins; Joanna M Biernacka
Journal:  Genet Epidemiol       Date:  2015-12-07       Impact factor: 2.135

8.  Evaluating the Calibration and Power of Three Gene-Based Association Tests of Rare Variants for the X Chromosome.

Authors:  Clement Ma; Michael Boehnke; Seunggeun Lee
Journal:  Genet Epidemiol       Date:  2015-10-10       Impact factor: 2.135

Review 9.  Update on the Clinical, Radiographic, and Neurobehavioral Manifestations in FXTAS and FMR1 Premutation Carriers.

Authors:  Deborah A Hall; Erin Robertson; Annie L Shelton; Molly C Losh; Montserrat Mila; Esther Granell Moreno; Beatriz Gomez-Anson; Verónica Martínez-Cerdeño; Jim Grigsby; Reymundo Lozano; Randi Hagerman; Lorena Santa Maria; Elizabeth Berry-Kravis; Joan A O'Keefe
Journal:  Cerebellum       Date:  2016-10       Impact factor: 3.847

10.  X-inactivation informs variance-based testing for X-linked association of a quantitative trait.

Authors:  Li Ma; Gabriel Hoffman; Alon Keinan
Journal:  BMC Genomics       Date:  2015-03-25       Impact factor: 3.969

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