A statistical measure for the skewness of X chromosome inactivation for quantitative traits and its application to the MCTFR data

Baohui Li1, Wen-Yi Yu1, Jianli Zhou1
1Department of Biostatistics, State Key Laboratory of Organ Failure Research, Ministry of Education, and Guangdong Provincial Key Laboratory of Tropical Disease Research, School of Public Health, Southern Medical University, No. 1023, South Shatai Road, Baiyun District, Guangzhou, 510515, Guangdong, China

Tóm tắt

Abstract Background X chromosome inactivation (XCI) is that one of two chromosomes in mammalian females is silenced during early development of embryos. There has been a statistical measure for the degree of the skewness of XCI for qualitative traits. However, no method is available for such task at quantitative trait loci. Results In this article, we extend the existing statistical measure for the skewness of XCI for qualitative traits, and the likelihood ratio, Fieller’s and delta methods for constructing the corresponding confidence intervals, and make them accommodate quantitative traits. The proposed measure is a ratio of two linear regression coefficients when association exists. Noting that XCI may cause variance heterogeneity of the traits across different genotypes in females, we obtain the point estimate and confidence intervals of the measure by incorporating such information. The hypothesis testing of the proposed methods is also investigated. We conduct extensive simulation studies to assess the performance of the proposed methods. Simulation results demonstrate that the median of the point estimates of the measure is very close to the pre-specified true value. The likelihood ratio and Fieller’s methods control the size well, and have the similar test power and accurate coverage probability, which perform better than the delta method. So far, we are not aware of any association study for the X-chromosomal loci in the Minnesota Center for Twin and Family Research data. So, we apply our proposed methods to these data for their practical use and find that only the rs792959 locus, which is simultaneously associated with the illicit drug composite score and behavioral disinhibition composite score, may undergo XCI skewing. However, this needs to be confirmed by molecular genetics. Conclusions We recommend the Fieller’s method in practical use because it is a non-iterative procedure and has the similar performance to the likelihood ratio method.

Từ khóa


Tài liệu tham khảo

Chabchoub G, Uz E, Maalej A, Mustafa CA, Rebai A, Mnif M, et al. Analysis of skewed X-chromosome inactivation in females with rheumatoid arthritis and autoimmune thyroid diseases. Arthritis Res Ther. 2009;11(4):R106. https://doi.org/10.1186/ar2759.

Ortona E, Pierdominici M, Maselli A, Veroni C, Aloisi F, Shoenfeld Y. Sex-based differences in autoimmune diseases. Ann Ist Super Sanita. 2016;52(2):205–12. https://doi.org/10.4415/ANN_16_02_12.

Brasch-Andersen C, Møller MU, Haagerup A, Vestbo J, Kruse TA. Evidence for an asthma risk locus on chromosome Xp: a replication linkage study. Allergy. 2008;63(9):1235–8. https://doi.org/10.1111/j.1398-9995.2008.01699.x.

Jacobs PA, Hunt PA, Mayer M, Bart RD. Duchenne muscular dystrophy (DMD) in a female with an X/autosome translocation: further evidence that the DMD locus is at Xp21. Am J Hum Genet. 1981;33(4):513–8.

Quan F, Janas J, Toth-Fejel S, Johnson DB, Wolford JK, Popovich BW. Uniparental disomy of the entire X chromosome in a female with Duchenne muscular dystrophy. Am J Hum Genet. 1997;60(1):160–5.

Migeon BR, Moser HW, Moser AB, Axelman J, Sillence D, Norum RA. Adrenoleukodystrophy: evidence for X linkage, inactivation, and selection favoring the mutant allele in heterozygous cells. Proc Natl Acad Sci U S A. 1981;78(8):5066–70. https://doi.org/10.1073/pnas.78.8.5066.

Goodship J, Carter J, Espanol T, Boyd Y, Malcolm S, Levinsky RJ. Carrier detection in Wiskott-Aldrich syndrome: combined use of M27 beta for X-inactivation studies and as a linked probe. Blood. 1991;77(12):2677–81. https://doi.org/10.1182/blood.V77.12.2677.2677.

Spatz A, Borg C, Feunteun J. X-chromosome genetics and human cancer. Nat Rev Cancer. 2004;4(8):617–29. https://doi.org/10.1038/nrc1413.

Kristiansen M, Knudsen GP, Maguire P, Margolin S, Pedersen J, Lindblom A, et al. High incidence of skewed X chromosome inactivation in young patients with familial non-BRCA1/BRCA2 breast cancer. J Med Genet. 2005;42(11):877–80. https://doi.org/10.1136/jmg.2005.032433.

Li G, Su Q, Liu GQ, Gong L, Zhang W, Zhu SJ, et al. Skewed X chromosome inactivation of blood cells is associated with early development of lung cancer in females. Oncol Rep. 2006;16(4):859–64.

Medema RH, Burgering BM. The X factor: skewing X inactivation towards cancer. Cell. 2007;129(7):1253–4. https://doi.org/10.1016/j.cell.2007.06.008.

Panning B. X chromosome inactivation and breast cancer: epigenetic alteration in tumor initiation and progression. Toxicon. 2007;54(2):121–7.

Wise AL, Gyi L, Manolio TA. Exclusion: toward integrating the X chromosome in genome-wide association analyses. Am J Hum Genet. 2013;92(5):643–7. https://doi.org/10.1016/j.ajhg.2013.03.017.

Lyon MF. Gene action in the X-chromosome of the mouse (Mus musculus L.). Nature. 1961;190(4773):372–3. https://doi.org/10.1038/190372a0.

Lyon MF. X-chromosome inactivation and developmental patterns in mammals. Biol Rev Camb Philos Soc. 1972;47(1):1–35. https://doi.org/10.1111/j.1469-185X.1972.tb00969.x.

Kay GF, Barton SC, Surani MA, Rastan S. Imprinting and X chromosome counting mechanisms determine Xist expression in early mouse development. Cell. 1994;77(5):639–50. https://doi.org/10.1016/0092-8674(94)90049-3.

Wong CC, Caspi A, Williams B, Houts R, Craig IW, Mill J. A longitudinal twin study of skewed X chromosome-inactivation. PLoS One. 2011;6(3):e17873. https://doi.org/10.1371/journal.pone.0017873.

Manzardo AM, Henkhaus R, Hidaka B, Penick EC, Poje AB, Butler MG. X chromosome inactivation in women with alcoholism. Alcohol Clin Exp Res. 2012;36(8):1325–9. https://doi.org/10.1111/j.1530-0277.2012.01740.x.

Amos-Landgraf JM, Cottle A, Plenge RM, Friez M, Schwartz CE, Longshore J, et al. X chromosome-inactivation patterns of 1,005 phenotypically unaffected females. Am J Hum Genet. 2006;79(3):493–9. https://doi.org/10.1086/507565.

Kay GF. Xist and X chromosome inactivation. Mol Cell Endocrinol. 1998;140(1–2):71–6. https://doi.org/10.1016/S0303-7207(98)00032-X.

Sharp A, Robinson D, Jacobs P. Age- and tissue-specific variation of X chromosome inactivation ratios in normal women. Hum Genet. 2000;107(4):343–9. https://doi.org/10.1007/s004390000382.

Plenge RM, Stevenson RA, Lubs HA, Schwartz CE, Willard HF. Skewed X-chromosome inactivation is a common feature of X-linked mental retardation disorders. Am J Hum Genet. 2002;71(1):168–73. https://doi.org/10.1086/341123.

Minks J, Robinson WP, Brown CJ. A skewed view of X chromosome inactivation. J Clin Invest. 2008;118(1):20–3. https://doi.org/10.1172/JCI34470.

Peeters SB, Cotton AM, Brown CJ. Variable escape from X-chromosome inactivation: identifying factors that tip the scales towards expression. Bioessays. 2014;36(8):746–56. https://doi.org/10.1002/bies.201400032.

Berletch JB, Ma W, Yang F, Shendure J, Noble WS, Disteche CM, et al. Escape from X inactivation varies in mouse tissues. PLoS Genet. 2015;11(3):e1005079. https://doi.org/10.1371/journal.pgen.1005079.

Clayton D. Testing for association on the X chromosome. Biostatistics. 2008;9(4):593–600. https://doi.org/10.1093/biostatistics/kxn007.

Wang J, Yu R, Shete S. X-chromosome genetic association test accounting for X-inactivation, skewed X-inactivation, and escape from X-inactivation. Genet Epidemiol. 2014;38(6):483–93. https://doi.org/10.1002/gepi.21814.

Wang P, Xu SQ, Wang BQ, Fung WK, Zhou JY. A robust and powerful test for case-control genetic association study on X chromosome. Stat Methods Med Res. 2019;28(10–11):3260–72. https://doi.org/10.1177/0962280218799532.

Liu W, Wang BQ, Liu-Fu G, Fung WK, Zhou JY. X-chromosome genetic association test incorporating X-chromosome inactivation and imprinting effects. J Genet. 2019;98(4):99. https://doi.org/10.1007/s12041-019-1146-6.

Zhang Y, Xu SQ, Liu W, Fung WK, Zhou JY. A robust test for X-chromosome genetic association accounting for X-chromosome inactivation and imprinting. Genet Res. 2020;102:e2. https://doi.org/10.1017/S0016672320000026.

Zhang L, Martin ER, Morris RW, Li YJ. Association test for X-linked QTL in family-based designs. Am J Hum Genet. 2009;84(4):431–44. https://doi.org/10.1016/j.ajhg.2009.02.010.

Ma L, Hoffman G, Keinan A. X-inactivation informs variance-based testing for X-linked association of a quantitative trait. BMC Genomics. 2015;16(1):241. https://doi.org/10.1186/s12864-015-1463-y.

Gao F, Chang D, Biddanda A, Ma L, Guo Y, Zhou Z, et al. XWAS: a software toolset for genetic data analysis and association studies of the X chromosome. J Hered. 2015;106(5):666–71. https://doi.org/10.1093/jhered/esv059.

Deng WQ, Mao S, Kalnapenkis A, Esko T, Mägi R, Paré G, et al. Analytical strategies to include the X-chromosome in variance heterogeneity analyses: evidence for trait-specific polygenic variance structure. Genet Epidemiol. 2019;43(7):815–30. https://doi.org/10.1002/gepi.22247.

Wang P, Zhang Y, Wang BQ, Li JL, Wang YX, Pan D, et al. A statistical measure for the skewness of X chromosome inactivation based on case-control design. BMC Bioinformatics. 2019;20(1):11. https://doi.org/10.1186/s12859-018-2587-2.

McGue M, Keyes M, Sharma A, Elkins I, Legrand L, Johnson W, et al. The environments of adopted and non-adopted youth: evidence on range restriction from the sibling interaction and behavior study (SIBS). Behav Genet. 2007;37(3):449–62. https://doi.org/10.1007/s10519-007-9142-7.

Keyes MA, Malone SM, Elkins IJ, Legrand LN, McGue M, Iacono WG. The enrichment study of the Minnesota twin family study: increasing the yield of twin families at high risk for externalizing psychopathology. Twin Res Hum Genet. 2009;12(5):489–501. https://doi.org/10.1375/twin.12.5.489.

Hicks BM, Schalet BD, Malone SM, Iacono WG, McGue M. Psychometric and genetic architecture of substance use disorder and behavioral disinhibition measures for gene association studies. Behav Genet. 2011;41(4):459–75. https://doi.org/10.1007/s10519-010-9417-2.

Miller MB, Basu S, Cunningham J, Eskin E, Malone SM, Oetting WS, et al. The Minnesota center for twin and family research genome-wide association study. Twin Res Hum Genet. 2012;15(6):767–74. https://doi.org/10.1017/thg.2012.62.

Vrieze SI, McGue M, Iacono WG. The interplay of genes and adolescent development in substance use disorders: leveraging findings from GWAS meta-analyses to test developmental hypotheses about nicotine consumption. Hum Genet. 2012;131(6):791–801. https://doi.org/10.1007/s00439-012-1167-1.

McGue M, Zhang Y, Miller MB, Basu S, Vrieze S, Hicks B, et al. A genome-wide association study of behavioral disinhibition. Behav Genet. 2013;43(5):363–73. https://doi.org/10.1007/s10519-013-9606-x.

Vrieze SI, McGue M, Miller MB, Hicks BM, Iacono WG. Three mutually informative ways to understand the genetic relationships among behavioral disinhibition, alcohol use, drug use, nicotine use/dependence, and their co-occurrence: twin biometry, GCTA, and genome-wide scoring. Behav Genet. 2013;43(2):97–107. https://doi.org/10.1007/s10519-013-9584-z.

Derringer J, Corley RP, Haberstick BC, Young SE, Demmitt BA, Howrigan DP, et al. Genome-wide association study of behavioral disinhibition in a selected adolescent sample. Behav Genet. 2015;45(4):375–81. https://doi.org/10.1007/s10519-015-9705-y.

Purcell S, Neale B, Todd-Brown K, Thomas L, Ferreira MA, Bender D, et al. PLINK: a tool set for whole-genome association and population-based linkage analyses. Am J Hum Genet. 2007;81(3):559–75. https://doi.org/10.1086/519795.

Chung RH, Ma D, Wang K, Hedges DJ, Jaworski JM, Gilbert JR, et al. An X chromosome-wide association study in autism families identifies TBL1X as a novel autism spectrum disorder candidate gene in males. Mol Autism. 2011;2(1):18. https://doi.org/10.1186/2040-2392-2-18.

Team RC. R: a language and environment for statistical computing, vol. 2020. Vienna: R Foundation for Statistical Computing; 2013. http://www.r-project.org.