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[Preprint]. 2023 Jan 31:2023.01.28.23285140.

doi: 10.1101/2023.01.28.23285140.

Population analyses of mosaic X chromosome loss identify genetic drivers and widespread signatures of cellular selection

Aoxing Liu^{1

2

3}, Giulio Genovese^{2

4

5

3}, Yajie Zhao^{6

3}, Matti Pirinen^{1

7

8}, Maryam M Zekavat^{2

9}, Katherine Kentistou⁶, Zhiyu Yang¹, Kai Yu¹⁰, Caitlyn Vlasschaert¹¹, Xiaoxi Liu¹², Derek W Brown^{10

13}, Georgi Hudjashov¹⁴, Bryan Gorman^{15

16}, Joe Dennis¹⁷, Weiyin Zhou¹⁰, Yukihide Momozawa¹⁸, Saiju Pyarajan^{15

19}, Vlad Tuzov¹⁴, Fanny-Dhelia Pajuste¹⁴, Mervi Aavikko¹, Timo P Sipilä¹, Awaisa Ghazal¹, Wen-Yi Huang¹⁰, Neal Freedman¹⁰, Lei Song¹⁰, Eugene J Gardner⁶, Vijay G Sankaran^{2

20

21}, Aarno Palotie^{1

2

4

22}, Hanna M Ollila^{1

2

23

24}, Taru Tukiainen¹, Stephen J Chanock¹⁰, Reedik Mägi¹⁴, Pradeep Natarajan^{2

9

24}, Mark J Daly^{1

2

4

22}, Alexander Bick²⁵, Steven A McCarroll^{2

4

5}, Chikashi Terao^{12

26

27}, Po-Ru Loh^{2

19

28

29}, Andrea Ganna^{1

2

4

22

29}, John R B Perry^{6

29}, Mitchell J Machiela^{10

29}

Affiliations

¹ Institute for Molecular Medicine Finland (FIMM), HiLIFE, University of Helsinki, Helsinki, Finland.
² Program in Medical and Population Genetics, Broad Institute of MIT and Harvard, Cambridge, MA, USA.
³ These authors contributed equally: Aoxing Liu, Giulio Genovese, Yajie Zhao.
⁴ Stanley Center, Broad Institute of Harvard and MIT, Cambridge, MA, USA.
⁵ Department of Genetics, Harvard Medical School, Boston, MA, USA.
⁶ MRC Epidemiology Unit, Institute of Metabolic Science, University of Cambridge, Cambridge, UK.
⁷ Department of Publich Health, University of Helsinki, Helsinki, Finland.
⁸ Department of Mathematics and Statistics, University of Helsinki, Helsinki, Finland.
⁹ Cardiovascular Research Center, Massachusetts General Hospital, Boston, MA, USA.
¹⁰ Division of Cancer Epidemiology and Genetics, National Cancer Institute, Rockville, MD, USA.
¹¹ Department of Medicine, Queen's University, Kingston, ON, Canada.
¹² Laboratory for Statistical and Translational Genetics, RIKEN Center for Integrative Medical Sciences, Yokohama, Japan.
¹³ Cancer Prevention Fellowship Program, Division of Cancer Prevention, National Cancer Institute, Rockville, MD, USA.
¹⁴ Estonian Genome Centre, Institute of Genomics, University of Tartu, Tartu, Estonia.
¹⁵ Center for Data and Computational Sciences (C-DACS), VA Cooperative Studies Program, VA Boston Healthcare System, Boston, MA, USA.
¹⁶ Booz Allen Hamilton, McLean, VA, USA.
¹⁷ Centre for Cancer Genetic Epidemiology, Department of Public Health and Primary Care, University of Cambridge, Cambridge, UK.
¹⁸ Laboratory for Genotyping Development, RIKEN Center for Integrative Medical Sciences, Yokohama, Japan.
¹⁹ Department of Medicine, Brigham and Women's Hospital, Harvard Medical School, Boston, MA, USA.
²⁰ Division of Hematology/Oncology, Boston Children's Hospital, Harvard Medical School, Boston, MA, USA.
²¹ Department of Pediatric Oncology, Dana-Farber Cancer Institute, Harvard Medical School, Boston, MA, USA.
²² Analytic and Translational Genetics Unit, Massachusetts General Hospital, Boston, MA, USA.
²³ Anesthesia, Critical Care, and Pain Medicine, Massachusetts General Hospital, Boston, MA, USA.
²⁴ Center of Genomic Medicine, Massachusetts General Hospital, Boston, MA, USA.
²⁵ Division of Genetic Medicine, Department of Medicine, Vanderbilt University Medical Center, Nashville, TN, USA.
²⁶ Clinical Research Center, Shizuoka General Hospital, Shizuoka, Japan.
²⁷ Department of Applied Genetics, School of Pharmaceutical Sciences, University of Shizuoka, Shizuoka, Japan.
²⁸ Center for Data Sciences, Brigham and Women's Hospital, Harvard Medical School, Boston, MA, USA.
²⁹ These authors jointly supervised this work: Po-Ru Loh, Andrea Ganna, John R.B. Perry, Mitchell J. Machiela.

PMID: 36778285
PMCID: PMC9915812
DOI: 10.1101/2023.01.28.23285140

Population analyses of mosaic X chromosome loss identify genetic drivers and widespread signatures of cellular selection

Aoxing Liu et al. medRxiv. 2023.

[Preprint]. 2023 Jan 31:2023.01.28.23285140.

doi: 10.1101/2023.01.28.23285140.

Authors

Affiliations

¹ Institute for Molecular Medicine Finland (FIMM), HiLIFE, University of Helsinki, Helsinki, Finland.
² Program in Medical and Population Genetics, Broad Institute of MIT and Harvard, Cambridge, MA, USA.
³ These authors contributed equally: Aoxing Liu, Giulio Genovese, Yajie Zhao.
⁴ Stanley Center, Broad Institute of Harvard and MIT, Cambridge, MA, USA.
⁵ Department of Genetics, Harvard Medical School, Boston, MA, USA.
⁶ MRC Epidemiology Unit, Institute of Metabolic Science, University of Cambridge, Cambridge, UK.
⁷ Department of Publich Health, University of Helsinki, Helsinki, Finland.
⁸ Department of Mathematics and Statistics, University of Helsinki, Helsinki, Finland.
⁹ Cardiovascular Research Center, Massachusetts General Hospital, Boston, MA, USA.
¹⁰ Division of Cancer Epidemiology and Genetics, National Cancer Institute, Rockville, MD, USA.
¹¹ Department of Medicine, Queen's University, Kingston, ON, Canada.
¹² Laboratory for Statistical and Translational Genetics, RIKEN Center for Integrative Medical Sciences, Yokohama, Japan.
¹³ Cancer Prevention Fellowship Program, Division of Cancer Prevention, National Cancer Institute, Rockville, MD, USA.
¹⁴ Estonian Genome Centre, Institute of Genomics, University of Tartu, Tartu, Estonia.
¹⁵ Center for Data and Computational Sciences (C-DACS), VA Cooperative Studies Program, VA Boston Healthcare System, Boston, MA, USA.
¹⁶ Booz Allen Hamilton, McLean, VA, USA.
¹⁷ Centre for Cancer Genetic Epidemiology, Department of Public Health and Primary Care, University of Cambridge, Cambridge, UK.
¹⁸ Laboratory for Genotyping Development, RIKEN Center for Integrative Medical Sciences, Yokohama, Japan.
¹⁹ Department of Medicine, Brigham and Women's Hospital, Harvard Medical School, Boston, MA, USA.
²⁰ Division of Hematology/Oncology, Boston Children's Hospital, Harvard Medical School, Boston, MA, USA.
²¹ Department of Pediatric Oncology, Dana-Farber Cancer Institute, Harvard Medical School, Boston, MA, USA.
²² Analytic and Translational Genetics Unit, Massachusetts General Hospital, Boston, MA, USA.
²³ Anesthesia, Critical Care, and Pain Medicine, Massachusetts General Hospital, Boston, MA, USA.
²⁴ Center of Genomic Medicine, Massachusetts General Hospital, Boston, MA, USA.
²⁵ Division of Genetic Medicine, Department of Medicine, Vanderbilt University Medical Center, Nashville, TN, USA.
²⁶ Clinical Research Center, Shizuoka General Hospital, Shizuoka, Japan.
²⁷ Department of Applied Genetics, School of Pharmaceutical Sciences, University of Shizuoka, Shizuoka, Japan.
²⁸ Center for Data Sciences, Brigham and Women's Hospital, Harvard Medical School, Boston, MA, USA.
²⁹ These authors jointly supervised this work: Po-Ru Loh, Andrea Ganna, John R.B. Perry, Mitchell J. Machiela.

PMID: 36778285
PMCID: PMC9915812
DOI: 10.1101/2023.01.28.23285140

Abstract

Mosaic loss of the X chromosome (mLOX) is the most commonly occurring clonal somatic alteration detected in the leukocytes of women, yet little is known about its genetic determinants or phenotypic consequences. To address this, we estimated mLOX in >900,000 women across eight biobanks, identifying 10% of women with detectable X loss in approximately 2% of their leukocytes. Out of 1,253 diseases examined, women with mLOX had an elevated risk of myeloid and lymphoid leukemias and pneumonia. Genetic analyses identified 49 common variants influencing mLOX, implicating genes with established roles in chromosomal missegregation, cancer predisposition, and autoimmune diseases. Complementary exome-sequence analyses identified rare missense variants in FBXO10 which confer a two-fold increased risk of mLOX. A small fraction of these associations were shared with mosaic Y chromosome loss in men, suggesting different biological processes drive the formation and clonal expansion of sex chromosome missegregation events. Allelic shift analyses identified alleles on the X chromosome which are preferentially retained, demonstrating that variation at many loci across the X chromosome is under cellular selection. A novel polygenic score including 44 independent X chromosome allelic shift loci correctly inferred the retained X chromosomes in 80.7% of mLOX cases in the top decile. Collectively our results support a model where germline variants predispose women to acquiring mLOX, with the allelic content of the X chromosome possibly shaping the magnitude of subsequent clonal expansion.

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Figures

**Figure 1.. Theoretical framework of the mLOX study.**
Panel **(A)** depicts the etiologic process leading to detectable mosaic loss of the X chromosome (mLOX) in females. Detectable age-related mLOX develops only if the mutant haematopoietic stem cell (HSC) survives loss of the X chromosome and the mutation confers a proliferative advantage over normal cells. Panel **(B)** shows the statistical approaches used to discover the genetic determinants of mLOX. Variants associated with susceptibility to mLOX, acting as either *trans* or *cis* factors, are examined using a genome-wide association study (GWAS), for common variants with minor allele frequency (MAF) > 0.1%, and a gene-burden test performed for whole-exome sequencing (WES) data for rare variants with MAF < 0.1%. Among samples with detectable mLOX, allelic shift analysis is used to detect chromosome X alleles exhibiting *cis* selection, that is, more likely to be clonally selected for when detectable mLOX retains these alleles.

**Figure 2.. Common and rare genetic contributors to mLOX susceptibility.**
Panel (A) shows genome-wide association study −log₁₀(P) for the association of common variants (MAF>0.1%) with mLOX. Labels are only assigned for candidate genes of the top 10 lead variants from meta-analysis or the top 10 candidate genes from gene prioritization and the y-axis is log scale. Panel (B) presents gene burden test −log₁₀(P) for the rare variants (MAF<0.1%) associations with mLOX. The dashed lines denote the statistical significance, which is 5.0×10⁻⁸ for GWAS (A) and 1.2×10⁻⁶ for the gene-burden test (B).

**Figure 3.. Shared and distinct genetic contributors to mLOX susceptibility in women and mLOY susceptibility in men.**
Examination of the shared and distinct genetic contributors to mLOX in women and mLOY in men. Panel (A) is a scatterplot of mLOX susceptibility variants (N=49) and mLOY susceptibility variants (N=147) and their effects on mLOX and mLOY. Variants are assigned to mLOX specific, mLOY specific, and shared by applying a Bayesian model with posterior probability >95%. (B) Fine-mapping of imputed HLA alleles for mLOX and mLOY in FinnGen, for three HLA alleles that are significantly associated with mLOX from step-wise conditional analyses. Panel (C) and (D) depict phenotype associations for lead variants of 30 independent mLOX susceptibility loci that were assigned to either mLOX specific or shared with mLOY. (C) Phenotype associations (GWAS lead variants (r²>0.6)) from Open Targets genetics. To avoid the impact of pleiotropic effects, we categorized phenotypes into blood cell measurement, autoimmunity and allergy, neoplasm, and others. The association with each phenotype category was first examined at a variant level and then summarized over all variants assigned to the same category in terms of the relationship with mLOY. To avoid the associations driven by HLA signals, we excluded all identified variants from the extended MHC region (GRCh38: chr6:25.7–33.4 Mb). (D) Associations with nine blood cell count traits. The absolute Z scores were cropped to the range of [0–20].

**Figure 4.. Allelic shift of chromosome X alleles among mLOX cases.**
Panel (A) shows −log₁₀(P) of chromosome X variants from allelic shift analysis by meta-analyzing data of 83,320 mLOX cases from seven biobanks, with lead variants of 44 independent loci highlighted. The dashed line denotes the statistical significance (5.0×10⁻⁸, which is the same as the GWAS significance level) and the y axis is log scale. Panel (B) depicts associations of 43 allelic shift analysis lead variants with 19 blood cell phenotypes. One variant was dropped due to no appropriate proxy variant available in blood cell phenotype GWAS. The absolute Z scores were cropped to the range of [0–20]. Panel (C) is a scatterplot of lead variants identified from allelic shift analysis (N=44) and their effects from allelic shift analysis (x axis) and GWAS (y axis). Variants are categorized based on P values from GWAS. Panel (D) and (E) show the fraction of mLOX cases with the retained X chromosome correctly inferred using an X chromosome differential score constructed from allelic shift analysis signals. To avoid overfitting, the effects of 44 lead variants were estimated from allelic shift analysis of 56,319 mLOX cases from six biobanks excluding FinnGen while the prediction performance was tested in 27,001 FinnGen mLOX cases. Panel (D) stratifies prediction performance by differential quantile of each X chromosome prediction score. Panel (E) shows the contribution of each lead variant to the prediction, starting with the most significant variants.

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References

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This is a preprint.

Population analyses of mosaic X chromosome loss identify genetic drivers and widespread signatures of cellular selection

Affiliations

Population analyses of mosaic X chromosome loss identify genetic drivers and widespread signatures of cellular selection

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