![[PDF]](/pb-assets/Icons/adobe-pdf-icon-1498649896243.png){kind=icon}
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.
References
| 1. | Lyon, MF . Gene action in the X-chromosome of the mouse (Mus musculus L). Nature 1961; 190: 372–373. Google Scholar | Crossref | Medline | ISI |
| 2. | Plenge, RM, Stevenson, RA, Lubs, HA, et al. Skewed X-chromosome inactivation is a common feature of X-linked mental retardation disorders. Am J Hum Genet 2002; 71: 168–173. Google Scholar | Crossref | Medline | ISI |
| 3. | Talebizadeh, Z, Bittel, DC, Veatch, OJ, et al. Brief report: Nonrandom X chromosome inactivation in females with autism. J Autism Dev Disord 2005; 35: 675–681. Google Scholar | Crossref | Medline | ISI |
| 4. | Chabchoub, G, Uz, E, Maalej, A, et al. Analysis of skewed X-chromosome inactivation in females with rheumatoid arthritis and autoimmune thyroid diseases. Arthritis Res Ther 2009; 11: 1–8. Google Scholar | Crossref |
| 5. | Buller, RE, Sood, AK, Lallas, T, et al. Association between nonrandom X-chromosome inactivation and BRCA1 mutation in germline DNA of patients with ovarian cancer. J Natl Cancer I 1999; 91: 339–346. Google Scholar | Crossref | Medline |
| 6. | Kristiansen, M, Langerod, A, Knudsen, GP, et al. High frequency of skewed X inactivation in young breast cancer patients. J Med Genet 2002; 39: 30–33. Google Scholar | Crossref | Medline | ISI |
| 7. | Brown, CJ, Carrel, L, Willard, HF. Expression of genes from the human active and inactive X chromosomes. Am J Med Genet A 1997; 60: 1333–1343. Google Scholar |
| 8. | Carrel, L, Willard, HF. X-inactivation profile reveals extensive variability in X-linked gene expression in females. Nature 2005; 434: 400–404. Google Scholar | Crossref | Medline | ISI |
| 9. | Carrel, L, Park, C, Tyekucheva, S, et al. Genomic environment predicts expression patterns on the human inactive X chromosome. Plos Genet 2006; 2: 1477–1486. Google Scholar | Crossref |
| 10. | Miller, AP, Willard, HF. Chromosomal basis of X chromosome inactivation: Identification of a multigene domain in Xp11.21-p11.22 that escapes X inactivation. P Natl Acad SCI USA 1998; 95: 8709–8714. Google Scholar | Crossref | Medline | ISI |
| 11. | Willard HF. The sex chromosomes and X chromosome inactivation. The metabolic and molecular bases of inherited disease, 1995; 719–737. Google Scholar |
| 12. | Kent, WJ, Sugnet, CW, Furey, TS, et al. The human genome browser at UCSC. Genome Res 2002; 12: 996–1006. Google Scholar | Crossref | Medline | ISI |
| 13. | Erdmann, J, Grosshennig, A, Braund, PS, et al. New susceptibility locus for coronary artery disease on chromosome 3q22. Nat Genet 2009; 41: 280–282. Google Scholar | Crossref | Medline | ISI |
| 14. | Samani, NJ, Erdmann, J, Hall, AS, et al. Genomewide association analysis of coronary artery disease. New Engl J Med 2007; 357: 443–453. Google Scholar | Crossref | Medline | ISI |
| 15. | Kathiresan, S, Willer, CJ, Peloso, GM, et al. Common variants at 30 loci contribute to polygenic dyslipidemia. Nat Genet 2009; 41: 56–65. Google Scholar | Crossref | Medline | ISI |
| 16. | Zheng, G, Joo, J, Zhang, C, et al. Testing association for markers on the X-chromosome. Genet Epidemiol 2007; 31: 834–843. Google Scholar | Crossref | Medline | ISI |
| 17. | Clayton, D . Testing for association on the X-chromosome. Biostatistics 2008; 9: 593–600. Google Scholar | Crossref | Medline | ISI |
| 18. | Browning, SR, Briley, JD, Briley, LP, et al. Case-control single-marker and haplotypic association analysis of pedigree data. Genet Epidemiol 2005; 28: 110–122. Google Scholar | Crossref | Medline |
| 19. | Thornton, T, Zhang, Q, Cai, X, et al. XM: Association testing on the X-chromosome in case-control samples with related individuals. Genet Epidemiol 2012; 36: 438–450. Google Scholar | Crossref | Medline |
| 20. | Chang, D, Gao, F, Slavney, A, et al. Accounting for eXentricities: Analysis of the X-chromosome in GWAS reveals X-linked genes implicated in autoimmune diseases. PLoS One 2014; 9: 1–31. Google Scholar | Crossref |
| 21. | Behrens, G, Winkler, TW, Gorski, M, et al. To stratify or not to stratify: Power considerations for population-based genome-wide association studies of quantitative traits. Genet Epidemiol 2011; 35: 867–879. Google Scholar | Crossref | Medline |
| 22. | 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: 483–493. Google Scholar | Crossref | Medline | ISI |
| 23. | Ma, L, Hoffman, G, Keinan, A. X-inactivation informs variance-based testing for X-linked association of a quantitative trait. BMC Genet 2015; 16: 777–780. Google Scholar |
| 24. | Gao, F, Chang, D, Biddanda, A, et al. XWAS: A toolset for genetic data analysis and association studies of the X-chromosome. J Hered 2015; 106: 666–671. Google Scholar | Crossref | Medline |
| 25. | Xu, W, Hao, M. A unified partial likelihood approach for X-chromosome association studies on time to event outcomes. Genet Epidemiol 2018; 42: 80–94. Google Scholar | Crossref | Medline |
| 26. | Xu, W, Hao, M. Partial likelihood ratio test for X-chromosome association models. Genet Epidemiol 42: 846–848. Google Scholar | Crossref | Medline |
| 27. | Tukiainen, T, Villani, AC, Yen, A, et al. Landscape of X chromosome inactivation across human tissues. Nature 2017; 550: 244–248. Google Scholar | Crossref | Medline |
| 28. | Garagnani, P, Bacalini, MG, Pirazzini, C, et al. Methylation of ELOVL 2 gene as a new epigenetic marker of age. Aging cell 2012; 11: 1132–1134. Google Scholar | Crossref | Medline |
| 29. | Knudsen, GPS, Pedersen, J, Klingenberg, O, et al. Increased skewing of X chromosome inactivation with age in both blood and buccal cells. Cytogenet Genome Res 2007; 116: 24–28. Google Scholar | Crossref | Medline |
| 30. | Liu, L, Huang, X. The use of Gaussian quadrature for estimation in frailty proportional hazards models. Stat Med 2008; 27: 2665–2683. Google Scholar | Crossref | Medline |
| 31. | Liu, L, Huang, X, Yaroshinsky, A, et al. Joint frailty models for zero-inflated recurrent events in the presence of a terminal event. Biometrics 2016; 72: 204–214. Google Scholar | Crossref | Medline |
| 32. | Liu, L . Joint modeling longitudinal semi-continuous data and survival, with application to longitudinal medical cost data. Stat Med 2009; 28: 972–986. Google Scholar | Crossref | Medline |
| 33. | Liu, L, Huang, X. Joint analysis of correlated repeated measures and recurrent events processes in the presence of a dependent terminal event. J Appl Stat 2009; 58: 65–81. Google Scholar |
| 34. | Liu, L, Huang, X, O'Quigley, J. Analysis of longitudinal data in the presence of informative observational times and a dependent terminal event, with application to medical cost data. Biometrics 2008; 64: 950–958. Google Scholar | Crossref | Medline |
| 35. | Zheng, S, Sun, J, Wiklund, F, et al. Cumulative association of five genetic variants with prostate cancer. New Engl J Med 2008; 358: 910–919. Google Scholar | Crossref | Medline | ISI |
| 36. | Salinas, CA, Koopmeiners, JS, Kwon, EM, et al. Clinical utility of five genetic variants for predicting prostate cancer risk and mortality. Prostate 2009; 69: 363–372. Google Scholar | Crossref | Medline | ISI |
| 37. | Zhang, K, Civan, J, Mukherjee, S, et al. Genetic variations in colorectal cancer risk and clinical outcome. World J Gastroentero 2014; 20: 4167–4177. Google Scholar | Crossref | Medline |
| 38. | Karrison, TG, Maitland, ML, Stadler, WM, et al. Design of phase II cancer trials using a continuous endpoint of change in tumor size: Application to a study of sorafenib and erlotinib in non small-cell lung cancer. J Natl Cancer I 2007; 99: 1455–1461. Google Scholar | Crossref | Medline |
| 39. | Baldwin, RM, Owzar, K, Zembutsu, H, et al. A genome-wide association study identifies novel loci for paclitaxel-induced sensory peripheral neuropathy in CALGB 40101. Clin Cancer Res 2012; 18: 5099–5109. Google Scholar | Crossref | Medline |
| 40. | Innocenti, F, Owzar, K, Cox, NL, et al. A genome-wide association study of overall survival in pancreatic cancer patients treated with gemcitabine in CALGB 80303. Clin Cancer Res 2012; 18: 577–584. Google Scholar | Crossref | Medline | ISI |
