AIJ: joint test for simultaneous detection of imprinting and non-imprinting allelic expression imbalance

Dao-Peng Chen; Fangyuan Zhang; Shili Lin; Dao-Peng Chen; Fangyuan Zhang; Shili Lin

doi:10.3934/mbe.2020020

Mathematical Biosciences and Engineering

2020, Volume 17, Issue 1: 366-386. doi: 10.3934/mbe.2020020

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AIJ: joint test for simultaneous detection of imprinting and non-imprinting allelic expression imbalance

1.
Institute of Biomedical Sciences, Academia Sinica, Taipei, Taiwan
2.
Department of Mathematics and Statistics, Texas Tech University, Texas, USA
3.
Department of Statistics, The Ohio State University, Ohio, USA

Received: 06 April 2019 Accepted: 24 September 2019 Published: 10 October 2019

Epigenetics is the study of heritable changes in gene expression or cellular phenotype caused by mechanisms other than changes in the underlying DNA sequence. Genomic imprinting is an epigenetically regulated process by which imprinted genes are expressed in a parent-of-origin-specific manner. It can be confounded with a phenomenon, allelic expression imbalance (AEI), which, in this paper, refers to asymmetric expression of the two alleles of a heterozygous subject at a single nucleotide polymorphism not caused by imprinting (non-imprinting AEI). Since existing methods in the literature are not amenable to distinguishing imprinting from non-imprinting AEI for data without replicates, we propose AIJ, a joint test for simultaneous detection of imprinting and non-imprinting AEI that accounts for potential confounding using RNA-seq data based on a reciprocal cross design. Through a simulation study, we show that AIJ is more powerful compared to two frequently used methods that do not account for confounding. To illustrate the practical utility of AIJ, we applied the method to a mouse dataset and identified genes with the imprinting effect and/or non-imprinting AEI phenomenon, with some already confirmed in an existing database. The results are also largely consistent with a study on human data for a set of orthologous genes, affirming earlier conclusion in the literature that non-imprinting AEI events are evolutionarily conserved.
- Allelic expression imbalance,
- genomic imprinting,
- reciprocal cross design,
- RNA-seq
Citation: Dao-Peng Chen, Fangyuan Zhang, Shili Lin. AIJ: joint test for simultaneous detection of imprinting and non-imprinting allelic expression imbalance[J]. Mathematical Biosciences and Engineering, 2020, 17(1): 366-386. doi: 10.3934/mbe.2020020

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Abstract

Epigenetics is the study of heritable changes in gene expression or cellular phenotype caused by mechanisms other than changes in the underlying DNA sequence. Genomic imprinting is an epigenetically regulated process by which imprinted genes are expressed in a parent-of-origin-specific manner. It can be confounded with a phenomenon, allelic expression imbalance (AEI), which, in this paper, refers to asymmetric expression of the two alleles of a heterozygous subject at a single nucleotide polymorphism not caused by imprinting (non-imprinting AEI). Since existing methods in the literature are not amenable to distinguishing imprinting from non-imprinting AEI for data without replicates, we propose AIJ, a joint test for simultaneous detection of imprinting and non-imprinting AEI that accounts for potential confounding using RNA-seq data based on a reciprocal cross design. Through a simulation study, we show that AIJ is more powerful compared to two frequently used methods that do not account for confounding. To illustrate the practical utility of AIJ, we applied the method to a mouse dataset and identified genes with the imprinting effect and/or non-imprinting AEI phenomenon, with some already confirmed in an existing database. The results are also largely consistent with a study on human data for a set of orthologous genes, affirming earlier conclusion in the literature that non-imprinting AEI events are evolutionarily conserved.

References

[1]	A. D. Goldberg, C. D. Allis and E. Bernstein, Epigenetics: a landscape takes shape, Cell, 128 (2007), 635-638.
[2]	A. C. Ferguson-Smith, Genomic imprinting: the emergence of an epigenetic paradigm, Nat. Rev. Genet., 12 (2011), 565-575.
[3]	I. M. Morison, C. J. Paton and S. D. Cleverley, The imprinted gene and parent-of-origin effect database, Nucleic Acids Res., 29 (2001), 275-276.
[4]	D. Haig, Evolutionary conflicts in pregnancy and calcium metabolism - a review, Placenta, 25 Suppl A (2004), S10-S15.
[5]	D. H. Lim and E. R. Maher, Human imprinting syndromes, Epigenomics, 1 (2009), 347-369.
[6]	X. Wang, Q. Sun, S. D. McGrath, et al., Transcriptome-wide identification of novel imprinted genes in neonatal mouse brain, PloS One, 3 (2008), e3839.
[7]	C. Gregg, J. W. Zhang, B. Weissbourd, et al., High-resolution analysis of parent-of-origin allelic expression in the mouse brain, Science, 329 (2010), 643-648.
[8]	Y. Takada, R. Miyagi, A. Takahashi, et al., A generalized linear model for decomposing cisregulatory, parent-of-origin, and maternal effects on allele-specific gene expression, G3-Genes Genom. Genet., 7 (2017), 2227-2234.
[9]	C. Wang, Z. Wang, D. R. Prows, et al., A computational framework for the inheritance pattern of genomic imprinting for complex traits, Brief. Bioinform., 13 (2012), 34-45.
[10]	T. He, J. Sa, P.-S. Zhong, et al., Statistical dissection of cyto-nuclear epistasis subject to genomic imprinting in line crosses, PloS One, 9 (2014), e91702.
[11]	T.-J. Chuang, Y.-H. Tseng, C.-Y. Chen, et al., Assessment of imprinting-and genetic variationdependent monoallelic expression using reciprocal allele descendants between human family trios, Sci. Rep., 7 (2017), 7038.
[12]	K. D. Falkenberg, N. E. Braverman, A. B. Moser, et al., Allelic expression imbalance promoting a mutant PEX6 allele causes Zellweger spectrum disorder, Am. J. Hum. Genet., 101 (2017), 965- 976.
[13]	P. Llavona, M. Pinelli, M. Mutarelli, et al., Allelic expression imbalance in the human retinal transcriptome and potential impact on inherited retinal diseases, Genes, 8 (2017), 283.
[14]	W. Lin, H.-D. Lin, X.-Y. Guo, et al., Allelic expression imbalance polymorphisms in susceptibility chromosome regions and the risk and survival of breast cancer, Mol. Carcinogen., 56 (2017), 300- 311.
[15]	J. M. Gahan, M. M. Byrne, E. Connolly, et al., 39 Allelic expression imbalance at interleukin 18 and chemokine cxcl 16 in patients with acute coronary syndromes, HEART, 101 (2015), A22, Annual Conference and AGM of the Irish-Cardiac-Society, Killarney, IRELAND, OCT 08-10, 2015.
[16]	L. B. Hesson, D. Packham, C.-T. Kwok, et al., Lynch syndrome associated with two MLH1 promoter variants and allelic imbalance of MLH1 expression, Hum. Mutat., 36 (2015), 622-630.
[17]	S. E. McCormack and S. F. A. Grant, Allelic expression imbalance: tipping the scales to elucidate the function of type 2 diabetes-associated loci, Diabetes, 64 (2015), 1102-1104.
[18]	A. K. Mondal, N. K. Sharma, S. C. Elbein, et al., Allelic expression imbalance screening of genes in chromosome 1q21-24 region to identify functional variants for Type 2 diabetes susceptibility, Physiol. Genomics, 45 (2013), 509-520.
[19]	J. Hu and I. Peter, Evidence of expression variation and allelic imbalance in Crohn's disease susceptibility genes NOD2 and ATG16L1 in human dendritic cells, Gene, 527 (2013), 496-502.
[20]	P. Fontanillas, C. R. Landry, P. J. Wittkopp, et al., Key considerations for measuring allelic expression on a genomic scale using high-throughput sequencing, Mol. Ecol., 19 Suppl 1 (2010), 212-227.
[21]	T. Goovaerts, S. Steyaert, C. A. Vandenbussche, et al., A comprehensive overview of genomic imprinting in breast and its deregulation in cancer, Nat. Commun., 9 (2018), 4120.
[22]	F. Zou, W. Sun, J. J. Crowley, et al., A novel statistical approach for jointly analyzing RNA-seq data from F-1 reciprocal crosses and inbred lines, Genetics, 197 (2014), 389-399.
[23]	J. Feng, C. A. Meyer, Q. Wang, et al., Gene expression GFOLD: a generalized fold change for ranking differentially expressed genes from RNA-seq data, Bioinformatics, 28 (2012), 2782-2788.
[24]	S. A. Seesi, Y. T. Tiagueu, A. Zelikovsky, et al., Bootstrap-based differential gene expression analysis for RNA-Seq data with and without replicates, BMC Genomics, 15 (2014), 1-10.
[25]	S. Tarazona, F. Garcia, J. Dopazo, et al., Differential expression in RNA-seq: A matter of depth, Genome Res., 2213-2223.
[26]	E. Topol, Individualized medicine from pre-womb to tomb, Cell, 157 (2014), 241-253.
[27]	N. J. Schork, Time for one-person trials, Nature, 520 (2015), 609-611.
[28]	J. Z. B. Low, T. F. Khang and M. T. Tammi, Open Access CORNAS: coverage-dependent RNA-Seq analysis of gene expression data without biological replicates, BMC Bioinformatics, 18 (2017), 575.
[29]	B. E. Storer and C. Kim, Exact properties of some exact test statistics for comparing two binomial proportions, J. Am. Stat. Assoc., 85 (1990), 146-155.
[30]	R. Little, Testing the equality of two independent binomial proportionsam stat, Am. Stat., 43 (1989), 283-288.
[31]	H. Luo and S. Lin, Evaluation of classical statistical methods for analyzing bs-seq data, OBM Genet., 2 (2018), 053.
[32]	M. Gehring, V. Missirian and S. Henikoff, Genomic analysis of parent-of-origin allelic expression in arabidopsis thaliana seeds, PloS One, 6 (2011), e23687.
[33]	D.-P. Chen, Statistical power for RNA-seq data to detect two epigenetic phenomena, Ph.D thesis, The Ohio State University, 2013.

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