Journal articles: 'Genomic imprinting - Statistical methods'

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Cui, Yuehua, James M. Cheverud, and Rongling Wu. "A statistical model for dissecting genomic imprinting through genetic mapping." Genetica 130, no. 3 (September 6, 2006): 227–39. http://dx.doi.org/10.1007/s10709-006-9101-x.

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Spencer, Hamish G. "The Correlation Between Relatives on the Supposition of Genomic Imprinting." Genetics 161, no. 1 (May 1, 2002): 411–17. http://dx.doi.org/10.1093/genetics/161.1.411.

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Abstract Standard genetic analyses assume that reciprocal heterozygotes are, on average, phenotypically identical. If a locus is subject to genomic imprinting, however, this assumption does not hold. We incorporate imprinting into the standard quantitative-genetic model for two alleles at a single locus, deriving expressions for the additive and dominance components of genetic variance, as well as measures of resemblance among relatives. We show that, in contrast to the case with Mendelian expression, the additive and dominance deviations are correlated. In principle, this correlation allows imprinting to be detected solely on the basis of different measures of familial resemblances, but in practice, the standard error of the estimate is likely to be too large for a test to have much statistical power. The effects of genomic imprinting will need to be incorporated into quantitative-genetic models of many traits, for example, those concerned with mammalian birthweight.

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Li, Yao, Yunqian Guo, Jianxin Wang, Wei Hou, Myron N. Chang, Duanping Liao, and Rongling Wu. "A Statistical Design for Testing Transgenerational Genomic Imprinting in Natural Human Populations." PLoS ONE 6, no. 2 (February 25, 2011): e16858. http://dx.doi.org/10.1371/journal.pone.0016858.

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Market, Brenna A., Liyue Zhang, Lauren S. Magri, Michael C. Golding, and Mellissa RW Mann. "INVESTIGATING THE MOLECULAR AND DEVELOPMENTAL EFFECTS OF VARIOUS CULTURE REGIMES IN A MOUSE MODEL SYSTEM." Clinical & Investigative Medicine 31, no. 4 (August 1, 2008): 16. http://dx.doi.org/10.25011/cim.v31i4.4814.

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Background/Purpose: Genomic imprinting is a specialized transcriptional mechanism that results in the unequal expression of alleles based on their parent-of-origin [1]. Many imprinted genes are critical for proper embryonic and fetaldevelopment [2] and disruption of genomic imprinting are associated with many development disorders [3]. Recently, increased frequencies of imprinting disorders have been correlated with the use of assisted reproductive technologies (ARTs)[2]. Rigorous and thorough testing of ARTs is required to determine their influence on genomic imprinting and development. I hypothesize that imprinting maintenance mechanisms are disrupted during early mouse development by the environmental insult of culture media used in human ARTs, and that loss of imprinting correlates with delayed embryonic development. Methods: The specific aims of my project are to develop a method to evaluate the methylation and expression patterns of 4 known imprinted genes in individual blastocysts. Results: We have successfully developed a novel method to evaluate both imprinted methylation and expression from a single mouse blastocyst. This method has been tested and results compared to methods used to evaluate imprinted methylation and expression separately; we have determined that results obtained with a combined protocol are equivalent to either alone. I will use this method to evaluate relationships between development rates in culture andgenomic imprinting, as well as the effects of various culture media used formouse and human embryo culture on genomic imprinting. Conclusion: This analysis allow for a more comprehensive study ofthe effects of environmental insult on genomic imprinting and preimplantation embryo development. References: 1. Reik W, Walter J. Genomic imprinting:parental influence on the genome. Nat Rev Genet 2001;2:21-32. 2. Rodenhiser D, Mann M. Epigenetics andhuman disease: translating basic biology into clinical applications. CMAJ. 2006;174:341-8. 3.Paoloni-Giacobino A. Epigenetics in reproductive medicine. Pediatr Res 2007;61:51R-57R.

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He, Tao, Jian Sa, Ping-Shou Zhong, and Yuehua Cui. "Statistical Dissection of Cyto-Nuclear Epistasis Subject to Genomic Imprinting in Line Crosses." PLoS ONE 9, no. 3 (March 18, 2014): e91702. http://dx.doi.org/10.1371/journal.pone.0091702.

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Rienecker, Kira DA, Matthew J. Hill, and Anthony R. Isles. "Methods of epigenome editing for probing the function of genomic imprinting." Epigenomics 8, no. 10 (October 2016): 1389–98. http://dx.doi.org/10.2217/epi-2016-0073.

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Elbracht, Miriam, Deborah Mackay, Matthias Begemann, Karl Oliver Kagan, and Thomas Eggermann. "Disturbed genomic imprinting and its relevance for human reproduction: causes and clinical consequences." Human Reproduction Update 26, no. 2 (February 18, 2020): 197–213. http://dx.doi.org/10.1093/humupd/dmz045.

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Abstract BACKGROUND Human reproductive issues affecting fetal and maternal health are caused by numerous exogenous and endogenous factors, of which the latter undoubtedly include genetic changes. Pathogenic variants in either maternal or offspring DNA are associated with effects on the offspring including clinical disorders and nonviable outcomes. Conversely, both fetal and maternal factors can affect maternal health during pregnancy. Recently, it has become evident that mammalian reproduction is influenced by genomic imprinting, an epigenetic phenomenon that regulates the expression of genes according to their parent from whom they are inherited. About 1% of human genes are normally expressed from only the maternally or paternally inherited gene copy. Since numerous imprinted genes are involved in (embryonic) growth and development, disturbance of their balanced expression can adversely affect these processes. OBJECTIVE AND RATIONALE This review summarises current our understanding of genomic imprinting in relation to human ontogenesis and pregnancy and its relevance for reproductive medicine. SEARCH METHODS Literature databases (Pubmed, Medline) were thoroughly searched for the role of imprinting in human reproductive failure. In particular, the terms ‘multilocus imprinting disturbances, SCMC, NLRP/NALP, imprinting and reproduction’ were used in various combinations. OUTCOMES A range of molecular changes to specific groups of imprinted genes are associated with imprinting disorders, i.e. syndromes with recognisable clinical features including distinctive prenatal features. Whereas the majority of affected individuals exhibit alterations at single imprinted loci, some have multi-locus imprinting disturbances (MLID) with less predictable clinical features. Imprinting disturbances are also seen in some nonviable pregnancy outcomes, such as (recurrent) hydatidiform moles, which can therefore be regarded as a severe form of imprinting disorders. There is growing evidence that MLID can be caused by variants in the maternal genome altering the imprinting status of the oocyte and the embryo, i.e. maternal effect mutations. Pregnancies of women carrying maternal affect mutations can have different courses, ranging from miscarriages to birth of children with clinical features of various imprinting disorders. WIDER IMPLICATIONS Increasing understanding of imprinting disturbances and their clinical consequences have significant impacts on diagnostics, counselling and management in the context of human reproduction. Defining criteria for identifying pregnancies complicated by imprinting disorders facilitates early diagnosis and personalised management of both the mother and offspring. Identifying the molecular lesions underlying imprinting disturbances (e.g. maternal effect mutations) allows targeted counselling of the family and focused medical care in further pregnancies.

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Varrault, Annie, Emeric Dubois, Anne Le Digarcher, and Tristan Bouschet. "Quantifying Genomic Imprinting at Tissue and Cell Resolution in the Brain." Epigenomes 4, no. 3 (September 4, 2020): 21. http://dx.doi.org/10.3390/epigenomes4030021.

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Imprinted genes are a group of ~150 genes that are preferentially expressed from one parental allele owing to epigenetic marks asymmetrically distributed on inherited maternal and paternal chromosomes. Altered imprinted gene expression causes human brain disorders such as Prader-Willi and Angelman syndromes and additional rare brain diseases. Research data principally obtained from the mouse model revealed how imprinted genes act in the normal and pathological brain. However, a better understanding of imprinted gene functions calls for building detailed maps of their parent-of-origin-dependent expression and of associated epigenetic signatures. Here we review current methods for quantifying genomic imprinting at tissue and cell resolutions, with a special emphasis on methods to detect parent-of-origin dependent expression and their applications to the brain. We first focus on bulk RNA-sequencing, the main method to detect parent-of-origin-dependent expression transcriptome-wide. We discuss the benefits and caveats of bulk RNA-sequencing and provide a guideline to use it on F1 hybrid mice. We then review methods for detecting parent-of-origin-dependent expression at cell resolution, including single-cell RNA-seq, genetic reporters, and molecular probes. Finally, we provide an overview of single-cell epigenomics technologies that profile additional features of genomic imprinting, including DNA methylation, histone modifications and chromatin conformation and their combination into sc-multimodal omics approaches, which are expected to yield important insights into genomic imprinting in individual brain cells.

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Wang, Yuedong. "Statistical methods for detecting genomic alterations through array-based comparative genomic hybridization (CGH)." Frontiers in Bioscience 9, no. 1-3 (2004): 540. http://dx.doi.org/10.2741/1186.

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Suzuki, Yoshiyuki. "Statistical methods for detecting natural selection from genomic data." Genes & Genetic Systems 85, no. 6 (2010): 359–76. http://dx.doi.org/10.1266/ggs.85.359.

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Rosen, G. L., and S. D. Essinger. "Comparison of Statistical Methods to Classify Environmental Genomic Fragments." IEEE Transactions on NanoBioscience 9, no. 4 (December 2010): 310–16. http://dx.doi.org/10.1109/tnb.2010.2081375.

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Lee, Seungyeoun, and Heeju Lim. "Review of statistical methods for survival analysis using genomic data." Genomics & Informatics 17, no. 4 (December 31, 2019): e41. http://dx.doi.org/10.5808/gi.2019.17.4.e41.

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Soldatov, R. A., and A. A. Mironov. "Statistical methods of comparative genomic analysis based on diffusion processes." Biophysics 58, no. 2 (March 2013): 142–47. http://dx.doi.org/10.1134/s0006350913020206.

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Brideau, Chelsea M., Kirsten E. Eilertson, James A. Hagarman, Carlos D. Bustamante, and Paul D. Soloway. "Successful Computational Prediction of Novel Imprinted Genes from Epigenomic Features." Molecular and Cellular Biology 30, no. 13 (April 26, 2010): 3357–70. http://dx.doi.org/10.1128/mcb.01355-09.

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ABSTRACT Approximately 100 mouse genes undergo genomic imprinting, whereby one of the two parental alleles is epigenetically silenced. Imprinted genes influence processes including development, X chromosome inactivation, obesity, schizophrenia, and diabetes, motivating the identification of all imprinted loci. Local sequence features have been used to predict candidate imprinted genes, but rigorous testing using reciprocal crosses validated only three, one of which resided in previously identified imprinting clusters. Here we show that specific epigenetic features in mouse cells correlate with imprinting status in mice, and we identify hundreds of additional genes predicted to be imprinted in the mouse. We used a multitiered approach to validate imprinted expression, including use of a custom single nucleotide polymorphism array and traditional molecular methods. Of 65 candidates subjected to molecular assays for allele-specific expression, we found 10 novel imprinted genes that were maternally expressed in the placenta.

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LaFramboise, Thomas L., D. Neil Hayes, and Torstein Tengs. "Statistical Analysis of Genomic Tag Data." Statistical Applications in Genetics and Molecular Biology 3, no. 1 (January 8, 2004): 1–22. http://dx.doi.org/10.2202/1544-6115.1099.

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We present a series of statistical solutions to challenges that commonly arise in the production and analysis of genomic tag libraries. Tag libraries are collections of fragments of DNA or RNA, with each unique fragment often present in millions or billions of copies. Inferences can be made from data obtained by sequencing a subset of the library. The statistical approaches outlined in this paper are divided into three parts. First, we demonstrate the application of classical capture-recapture theory to the question of library complexity, i.e. the number of unique fragments in the library. Simulation studies verify the accuracy, for sample sizes of magnitudes typical in genomic studies, of the formulas we use to make our estimates. Second, we present a straightforward statistical cost analysis of tag experiments designed to uncover either disease-causing pathogens or new genes. Third, we develop a hidden Markov model approach to karyotyping a sample using a tag library derived from the sample's genomic DNA. While the resolution of the approach depends upon the number of tags sequenced from the library, we show via simulation that copy number alterations can be reliably detected for lengths as small as 1 Mb, even when a moderate number of tags are sequenced. Simulations predict very good specificity as well. Finally, all three of our approaches are applied to data from real tag library experiments. The hidden Markov model results are in line with what was expected from simulation, and genomic alterations found by applying the method to a cancer cell line library are confirmed using PCR.The methods and data described in this paper are contained in an R package, tagAnalysis, freely available at http://meyerson.dfci.harvard.edu/~tl974/tagAnalysis.

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Zhai, Weiwei, Rasmus Nielsen, Nick Goldman, and Ziheng Yang. "Looking for Darwin in Genomic Sequences—Validity and Success of Statistical Methods." Molecular Biology and Evolution 29, no. 10 (April 3, 2012): 2889–93. http://dx.doi.org/10.1093/molbev/mss104.

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Neves, Haroldo HR, Roberto Carvalheiro, and Sandra A. Queiroz. "A comparison of statistical methods for genomic selection in a mice population." BMC Genetics 13, no. 1 (2012): 100. http://dx.doi.org/10.1186/1471-2156-13-100.

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Mackin, Sarah-Jayne, Avinash Thakur, and Colum P. Walsh. "Imprint stability and plasticity during development." Reproduction 156, no. 2 (August 2018): R43—R55. http://dx.doi.org/10.1530/rep-18-0051.

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There have been a number of recent insights in the area of genomic imprinting, the phenomenon whereby one of two autosomal alleles is selected for expression based on the parent of origin. This is due in part to a proliferation of new techniques for interrogating the genome that are leading researchers working on organisms other than mouse and human, where imprinting has been most studied, to become interested in looking for potential imprinting effects. Here, we recap what is known about the importance of imprints for growth and body size, as well as the main types of locus control. Interestingly, work from a number of labs has now shown that maintenance of the imprint post implantation appears to be a more crucial step than previously appreciated. We ask whether imprints can be reprogrammed somatically, how many loci there are and how conserved imprinted regions are in other species. Finally, we survey some of the methods available for examining DNA methylation genome-wide and look to the future of this burgeoning field.

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Shao, Yongzhao, Wei Pan, and Xiaohua Douglas Zhang. "Advanced Designs and Statistical Methods for Genetic and Genomic Studies of Complex Diseases." Journal of Probability and Statistics 2012 (2012): 1–3. http://dx.doi.org/10.1155/2012/805426.

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de Koning, Dirk-Jan, Henk Bovenhuis, and Johan A. M. van Arendonk. "On the Detection of Imprinted Quantitative Trait Loci in Experimental Crosses of Outbred Species." Genetics 161, no. 2 (June 1, 2002): 931–38. http://dx.doi.org/10.1093/genetics/161.2.931.

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Abstract In this article, the quantitative genetic aspects of imprinted genes and statistical properties of methods to detect imprinted QTL are studied. Different models to detect imprinted QTL and to distinguish between imprinted and Mendelian QTL were compared in a simulation study. Mendelian and imprinted QTL were simulated in an F2 design and analyzed under Mendelian and imprinting models. Mode of expression was evaluated against the H0 of a Mendelian QTL as well as the H0 of an imprinted QTL. It was shown that imprinted QTL might remain undetected when analyzing the genome with Mendelian models only. Compared to testing against a Mendelian QTL, using the H0 of an imprinted QTL gave a higher proportion of correctly identified imprinted QTL, but also gave a higher proportion of false inference of imprinting for Mendelian QTL. When QTL were segregating in the founder lines, spurious detection of imprinting became more prominent under both tests, especially for designs with a small number of F1 sires.

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Cui, Zhe, Jayaram Kancherla, Kyle W. Chang, Niklas Elmqvist, and Héctor Corrada Bravo. "Proactive visual and statistical analysis of genomic data in Epiviz." Bioinformatics 36, no. 7 (November 29, 2019): 2195–201. http://dx.doi.org/10.1093/bioinformatics/btz883.

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Abstract Motivation Integrative analysis of genomic data that includes statistical methods in combination with visual exploration has gained widespread adoption. Many existing methods involve a combination of tools and resources: user interfaces that provide visualization of large genomic datasets, and computational environments that focus on data analyses over various subsets of a given dataset. Over the last few years, we have developed Epiviz as an integrative and interactive genomic data analysis tool that incorporates visualization tightly with state-of-the-art statistical analysis framework. Results In this article, we present Epiviz Feed, a proactive and automatic visual analytics system integrated with Epiviz that alleviates the burden of manually executing data analysis required to test biologically meaningful hypotheses. Results of interest that are proactively identified by server-side computations are listed as notifications in a feed. The feed turns genomic data analysis into a collaborative work between the analyst and the computational environment, which shortens the analysis time and allows the analyst to explore results efficiently. We discuss three ways where the proposed system advances the field of genomic data analysis: (i) takes the first step of proactive data analysis by utilizing available CPU power from the server to automate the analysis process; (ii) summarizes hypothesis test results in a way that analysts can easily understand and investigate; (iii) enables filtering and grouping of analysis results for quick search. This effort provides initial work on systems that substantially expand how computational and visualization frameworks can be tightly integrated to facilitate interactive genomic data analysis. Availability and implementation The source code for Epiviz Feed application is available at http://github.com/epiviz/epiviz_feed_polymer. The Epiviz Computational Server is available at http://github.com/epiviz/epiviz-feed-computation. Please refer to Epiviz documentation site for details: http://epiviz.github.io/.

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Wesseler, Katharina, Florian Kraft, and Thomas Eggermann. "Molecular and Clinical Opposite Findings in 11p15.5 Associated Imprinting Disorders: Characterization of Basic Mechanisms to Improve Clinical Management." International Journal of Molecular Sciences 20, no. 17 (August 28, 2019): 4219. http://dx.doi.org/10.3390/ijms20174219.

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Silver–Russell and Beckwith–Wiedemann syndromes (SRS, BWS) are rare congenital human disorders characterized by opposite growth disturbances. With the increasing knowledge on the molecular basis of SRS and BWS, it has become obvious that the disorders mirror opposite alterations at the same genomic loci in 11p15.5. In fact, these changes directly or indirectly affect the expression of IGF2 and CDKN1C and their associated pathways, and thereby, cause growth disturbances as key features of both diseases. The increase of knowledge has become possible with the development and implementation of new and comprehensive assays. Whereas, in the beginning molecular testing was restricted to single chromosomal loci, many tests now address numerous loci in the same run, and the diagnostic implementation of (epi)genome wide assays is only a question of time. These high-throughput approaches will be complemented by the analysis of other omic datasets (e.g., transcriptome, metabolome, proteome), and it can be expected that the integration of these data will massively improve the understanding of the pathobiology of imprinting disorders and their diagnostics. Especially long-read sequencing methods, e.g., nanopore sequencing, allowing direct detection of native DNA modification, will strongly contribute to a better understanding of genomic imprinting in the near future. Thereby, new genomic loci and types of pathogenic variants will be identified, resulting in more precise discrimination into different molecular subgroups. These subgroups serve as the basis for (epi)genotype–phenotype correlations, allowing a more directed prognosis, counseling, and therapy. By deciphering the pathophysiological consequences of SRS and BWS and their molecular disturbances, future therapies will be available targeting the basic cause of the disease and respective pathomechanisms and will complement conventional therapeutic strategies.

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Komori, Osamu, and Shinto Eguchi. "Prediction Analysis for Genomic and Proteomic Data: New Statistical Methods Based on Machine Learning." Japanese Journal of Biometrics 32, no. 1 (2011): 49–73. http://dx.doi.org/10.5691/jjb.32.49.

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Schaid, Daniel J. "Genomic Similarity and Kernel Methods I: Advancements by Building on Mathematical and Statistical Foundations." Human Heredity 70, no. 2 (2010): 109–31. http://dx.doi.org/10.1159/000312641.

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Mohammadi, Yahya, and Morteza Sattaei Mokhtari. "Genomic Selection Accuracy Parametric and Nonparametric Statistical Methods with Additive and Dominance Genetic Architectures." Research on Animal Production 8, no. 18 (March 1, 2018): 161–67. http://dx.doi.org/10.29252/rap.8.18.161.

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Urbut, Sarah M., Gao Wang, Peter Carbonetto, and Matthew Stephens. "Flexible statistical methods for estimating and testing effects in genomic studies with multiple conditions." Nature Genetics 51, no. 1 (November 26, 2018): 187–95. http://dx.doi.org/10.1038/s41588-018-0268-8.

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SEMIK-GURGUL, EWELINA, and TOMASZ ZĄBEK. "DNA methylation in the cancerogenesis process and methods of its detection." Medycyna Weterynaryjna 74, no. 6 (2018): 5982–2018. http://dx.doi.org/10.21521/mw.5982.

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Epigenetic modifications, apart from affecting gene expression, play an important role in the chromatin structure stabilization, embryonic development and the genomic imprinting. Recent studies have shown that they also play a vital role in other biological processes, including silencing of the expression and mobility of transposable elements and resistance to viral infections by blocking the expression of viral genes. The stability of the genome and the expression of genes in normal cells are strongly dependent on the DNA methylation pattern, which is visibly disturbed in tumor cells. These alterations may be a consequence of the attachment of methyl groups to cytosines in unmethylated DNA sequences, resulting in an increase in the degree of methylation or can be a result of demethylation, i.e. a reduction in the level of DNA methylation. Currently, many techniques are available to determine the level of methylcytosine in DNA, both at the level of single genes and the whole genome. However, each method has its advantages and disadvantages, not being universal in relation to the type of research material and the purpose of planned analyses..

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Karbalaie Niya, Mohammad Hadi, Naeimeh Roshan-zamir, and Elham Mortazavi. "DNA Methylation Tools and Strategies: Methods in a Review." Asian Pacific Journal of Cancer Biology 4, no. 3 (September 8, 2019): 51–57. http://dx.doi.org/10.31557/apjcb.2019.4.3.51-57.

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DNA methylation is known as an important epigenetic change in plants and vertebrates genome. In this process, the methyl group transferred by DNA methyl transferase enzymes to cytosine at carbon residue 5 often in the CpG dinucleotide context. DNA methylation plays an important role in the natural development of the organism, genome stability maintenance and processes such as genomic imprinting and chromosome X inactivation in mammals. In addition, changes in DNA methylation pattern have seen in many diseases, including cancer. Analysis of DNA methylation has been useful for rapid disease diagnosis and progression. In recent decades, a revolution has taken place in the methods of DNA methylation analysis, and it is possible to study the pattern of gene methylation at a widespread, short and high resolution level. These methods can be divided into three general categories: (1) cut-based methods by methylation-sensitive enzymes; (2) sodium bisulfide based methods; (3) antibody based methods. Since the existence of different methods makes it difficult to select the appropriate approach, in this review, a number of common methods for examining the methylation pattern with the advantages and disadvantages will be discussed.

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Liu, Ye, and Douglas Forrest. "Identification of Cell Types that Express Dio3 Deiodinase, a Thyroid Hormone-Inactivating Enzyme, Using a Dio3-CreERT2 Reporter System." Journal of the Endocrine Society 5, Supplement_1 (May 1, 2021): A975. http://dx.doi.org/10.1210/jendso/bvab048.1993.

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Abstract Background: Thyroid hormone promotes development, growth and metabolism. The level of thyroid hormone ligand (triiodothyronine, T3) in tissues depends not only on circulating levels but also upon tight regulation by activating and inactivating deiodinases within tissues. Type 3 deiodinase (Dio3) inactivates T3 and its precursor thyroxine (T4) and mediates many functions including in neurodevelopmental, sensory and reproductive systems. Dio3 is subject to genomic imprinting. Despite its critical functions, Dio3 is often expressed transiently and at low levels in restricted cell populations making it difficult to detect in natural tissues. Methods: To visualize Dio3 expression at cellular resolution, we derived a Dio3-CreERt2 knockin allele that expresses tamoxifen-dependent Cre recombinase from the endogenous Dio3 gene. When crossed with Ai6 reporter mice, Dio3-CreERt2-positive cells display fluorescent signals. When tamoxifen-treated at neonatal ages, Dio3-CreERt2 recapitulates endogenous Dio3 expression as previously reported in brain: in the bed nucleus of the stria terminalis and preoptic nuclei. In addition, we uncovered several positive cell groups in the hypothalamus, brain stem, pituitary and other tissues. Drastic differences were observed for Dio3-CreERt2 as a paternally versus maternally inherited allele, revealing imprinting effect in specific cell types. Dio3-CreERT2 activity is enhanced by T3 administration, in accordance with Dio3 as a T3-indicible gene. Conclusion: The Dio3-CreERT2 model sensitively reveals Dio3-expressing cell types in tissues. The model is useful for studying expression patterns, imprinting and lineage tracing of Dio3-positive cells during development and homeostatic challenges.

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Montesinos-López, Abelardo, Osval Antonio Montesinos-López, José Cricelio Montesinos-López, Carlos Alberto Flores-Cortes, Roberto de la Rosa, and José Crossa. "A guide for kernel generalized regression methods for genomic-enabled prediction." Heredity 126, no. 4 (March 1, 2021): 577–96. http://dx.doi.org/10.1038/s41437-021-00412-1.

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AbstractThe primary objective of this paper is to provide a guide on implementing Bayesian generalized kernel regression methods for genomic prediction in the statistical software R. Such methods are quite efficient for capturing complex non-linear patterns that conventional linear regression models cannot. Furthermore, these methods are also powerful for leveraging environmental covariates, such as genotype × environment (G×E) prediction, among others. In this study we provide the building process of seven kernel methods: linear, polynomial, sigmoid, Gaussian, Exponential, Arc-cosine 1 and Arc-cosine L. Additionally, we highlight illustrative examples for implementing exact kernel methods for genomic prediction under a single-environment, a multi-environment and multi-trait framework, as well as for the implementation of sparse kernel methods under a multi-environment framework. These examples are followed by a discussion on the strengths and limitations of kernel methods and, subsequently by conclusions about the main contributions of this paper.

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Beichman, Annabel C., Emilia Huerta-Sanchez, and Kirk E. Lohmueller. "Using Genomic Data to Infer Historic Population Dynamics of Nonmodel Organisms." Annual Review of Ecology, Evolution, and Systematics 49, no. 1 (November 2, 2018): 433–56. http://dx.doi.org/10.1146/annurev-ecolsys-110617-062431.

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Genome sequence data are now being routinely obtained from many nonmodel organisms. These data contain a wealth of information about the demographic history of the populations from which they originate. Many sophisticated statistical inference procedures have been developed to infer the demographic history of populations from this type of genomic data. In this review, we discuss the different statistical methods available for inference of demography, providing an overview of the underlying theory and logic behind each approach. We also discuss the types of data required and the pros and cons of each method. We then discuss how these methods have been applied to a variety of nonmodel organisms. We conclude by presenting some recommendations for researchers looking to use genomic data to infer demographic history.

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Li, Gengxin, and Yuehua Cui. "A Statistical Variance Components Framework for Mapping Imprinted Quantitative Trait Locus in Experimental Crosses." Journal of Probability and Statistics 2009 (2009): 1–27. http://dx.doi.org/10.1155/2009/689489.

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Current methods for mapping imprinted quantitative trait locus (iQTL) with inbred line crosses assume fixed QTL effects. When an iQTL segregates in experimental line crosses, combining different line crosses with similar genetic background can improve the accuracy of iQTLs inference. In this article, we develop a general interval-based statistical variance components framework to map iQTLs underlying complex traits by combining different backcross line crosses. We propose a new iQTL variance partition method based on the nature of marker alleles shared identical-by-decent (IBD) in inbred lines. Maternal effect is adjusted when testing imprinting. Efficient estimation methods with the maximum likelihood and the restricted maximum likelihood are derived and compared. Statistical properties of the proposed mapping strategy are evaluated through extensive simulations under different sampling designs. An extension to multiple QTL analysis is given. The proposed method will greatly facilitate genetic dissection of imprinted complex traits in inbred line crosses.

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Piccoli, Mario L., Luiz F. Brito, José Braccini, Fernanda V. Brito, Fernando F. Cardoso, Jaime A. Cobuci, Mehdi Sargolzaei, and Flávio S. Schenkel. "A comprehensive comparison between single- and two-step GBLUP methods in a simulated beef cattle population." Canadian Journal of Animal Science 98, no. 3 (September 1, 2018): 565–75. http://dx.doi.org/10.1139/cjas-2017-0176.

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The statistical methods used in the genetic evaluations are a key component of the process and can be best compared by using simulated data. The latter is especially true in grazing beef cattle production systems, where the number of proven bulls with highly reliable estimated breeding values is limited to allow for a trustworthy validation of genomic predictions. Therefore, we simulated data for 4980 beef cattle aiming to compare single-step genomic best linear unbiased prediction (ssGBLUP), which simultaneously incorporates pedigree, phenotypic, and genomic data into genomic evaluations, and two-step GBLUP (tsGBLUP) procedures and genomic estimated breeding values (GEBVs) blending methods. The greatest increases in GEBV accuracies compared with the parents’ average estimated breeding values (EBVPA) were 0.364 and 0.341 for ssGBLUP and tsGBLUP, respectively. Direct genomic value and GEBV accuracies when using ssGBLUP and tsGBLUP procedures were similar, except for the GEBV accuracies using Hayes’ blending method in tsGBLUP. There was no significant or slight bias in genomic predictions from ssGBLUP or tsGBLUP (using VanRaden’s blending method), indicating that these predictions are on the same scale compared with the true breeding values. Overall, genetic evaluations including genomic information resulted in gains in accuracy >100% compared with the EBVPA. In addition, there were no significant differences between the selected animals (10% males and 50% females) by using ssGBLUP or tsGBLUP.

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Ye, Shuyun, John A. Dawson, and Christina Kendziorski. "Extending Information Retrieval Methods to Personalized Genomic-Based Studies of Disease." Cancer Informatics 13s7 (January 2014): CIN.S16354. http://dx.doi.org/10.4137/cin.s16354.

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Genomic-based studies of disease now involve diverse types of data collected on large groups of patients. A major challenge facing statistical scientists is how best to combine the data, extract important features, and comprehensively characterize the ways in which they affect an individual's disease course and likelihood of response to treatment. We have developed a survival-supervised latent Dirichlet allocation (survLDA) modeling framework to address these challenges. Latent Dirichlet allocation (LDA) models have proven extremely effective at identifying themes common across large collections of text, but applications to genomics have been limited. Our framework extends LDA to the genome by considering each patient as a “document” with “text” detailing his/her clinical events and genomic state. We then further extend the framework to allow for supervision by a time-to-event response. The model enables the efficient identification of collections of clinical and genomic features that co-occur within patient subgroups, and then characterizes each patient by those features. An application of survLDA to The Cancer Genome Atlas ovarian project identifies informative patient subgroups showing differential response to treatment, and validation in an independent cohort demonstrates the potential for patient-specific inference.

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Cleary, Siobhan, and Cathal Seoighe. "Perspectives on Allele-Specific Expression." Annual Review of Biomedical Data Science 4, no. 1 (July 20, 2021): 101–22. http://dx.doi.org/10.1146/annurev-biodatasci-021621-122219.

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Diploidy has profound implications for population genetics and susceptibility to genetic diseases. Although two copies are present for most genes in the human genome, they are not necessarily both active or active at the same level in a given individual. Genomic imprinting, resulting in exclusive or biased expression in favor of the allele of paternal or maternal origin, is now believed to affect hundreds of human genes. A far greater number of genes display unequal expression of gene copies due to cis-acting genetic variants that perturb gene expression. The availability of data generated by RNA sequencing applied to large numbers of individuals and tissue types has generated unprecedented opportunities to assess the contribution of genetic variation to allelic imbalance in gene expression. Here we review the insights gained through the analysis of these data about the extent of the genetic contribution to allelic expression imbalance, the tools and statistical models for gene expression imbalance, and what the results obtained reveal about the contribution of genetic variants that alter gene expression to complex human diseases and phenotypes.

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Howard, Réka, Alicia L. Carriquiry, and William D. Beavis. "Parametric and Nonparametric Statistical Methods for Genomic Selection of Traits with Additive and Epistatic Genetic Architectures." G3: Genes|Genomes|Genetics 4, no. 6 (April 11, 2014): 1027–46. http://dx.doi.org/10.1534/g3.114.010298.

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Pinheiro, M., V. Afreixo, G. Moura, A. Freitas, M. A. S. Santos, and J. L. Oliveira. "Statistical, Computational and Visualization Methodologies to Unveil Gene Primary Structure Features." Methods of Information in Medicine 45, no. 02 (2006): 163–68. http://dx.doi.org/10.1055/s-0038-1634061.

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Summary Objectives: Gene sequence features such as codon bias, codon context, and codon expansion (e.g. tri-nucleotide repeats) can be better understood at the genomic scale level by combining statistical methodologies with advanced computer algorithms and data visualization through sophisticated graphical interfaces. This paper presents the ANACONDA system, a bioinformatics application for gene primary structure analysis. Methods: Codon usage tables using absolute metrics and software for multivariate analysis of codon and amino acid usage are available in public databases. However, they do not provide easy computational and statistical tools to carry out detailed gene primary structure analysis on a genomic scale. We propose the usage of several statistical methods – contingency table analysis, residual analysis, multivariate analysis (cluster analysis) – to analyze the codon bias under various aspects (degree of association, contexts and clustering). Results: The developed solution is a software application that provides a user-guided analysis of codon sequences considering several contexts and codon usage on a genomic scale. The utilization of this tool in our molecular biology laboratory is focused on particular genomes, especially those from Saccharomyces cerevisiae, Candida albicansand Escherichia coli. In order to illustrate the applicability and output layouts of the software these species are herein used as examples. Conclusions: The statistical tools incorporated in the system are allowing to obtain global views of important sequence features. It is expected that the results obtained will permit identification of general rules that govern codon context and codon usage in any genome. Additionally, identification of genes containing expanded codons that arise as a consequence of erroneous DNA replication events will permit uncovering new genes associated with human disease.

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Borchiellini, Marta, Simone Ummarino, and Annalisa Di Ruscio. "The Bright and Dark Side of DNA Methylation: A Matter of Balance." Cells 8, no. 10 (October 12, 2019): 1243. http://dx.doi.org/10.3390/cells8101243.

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DNA methylation controls several cellular processes, from early development to old age, including biological responses to endogenous or exogenous stimuli contributing to disease transition. As a result, minimal DNA methylation changes during developmental stages drive severe phenotypes, as observed in germ-line imprinting disorders, while genome-wide alterations occurring in somatic cells are linked to cancer onset and progression. By summarizing the molecular events governing DNA methylation, we focus on the methods that have facilitated mapping and understanding of this epigenetic mark in healthy conditions and diseases. Overall, we review the bright (health-related) and dark (disease-related) side of DNA methylation changes, outlining how bulk and single-cell genomic analyses are moving toward the identification of new molecular targets and driving the development of more specific and less toxic demethylating agents.

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Wei, Yingying. "Integrative Analyses of Cancer Data: A Review from a Statistical Perspective." Cancer Informatics 14s2 (January 2015): CIN.S17303. http://dx.doi.org/10.4137/cin.s17303.

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It has become increasingly common for large-scale public data repositories and clinical settings to have multiple types of data, including high-dimensional genomics, epigenomics, and proteomics data as well as survival data, measured simultaneously for the same group of biological samples, which provides unprecedented opportunities to understand cancer mechanisms from a more comprehensive scope and to develop new cancer therapies. Nevertheless, how to interpret a wealth of data into biologically and clinically meaningful information remains very challenging. In this paper, I review recent development in statistics for integrative analyses of cancer data. Topics will cover meta-analysis of homogeneous type of data across multiple studies, integrating multiple heterogeneous genomic data types, survival analysis with high- or ultrahigh-dimensional genomic profiles, and cross-data-type prediction where both predictors and responses are high- or ultrahigh-dimensional vectors. I compare existing statistical methods and comment on potential future research problems.

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Menzel, Michael, Peter Koch, Stefan Glasenhardt, and Andreas Gogol-Döring. "Enhort: a platform for deep analysis of genomic positions." PeerJ Computer Science 5 (June 10, 2019): e198. http://dx.doi.org/10.7717/peerj-cs.198.

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The rise of high-throughput methods in genomic research greatly expanded our knowledge about the functionality of the genome. At the same time, the amount of available genomic position data increased massively, e.g., through genome-wide profiling of protein binding, virus integration or DNA methylation. However, there is no specialized software to investigate integration site profiles of virus integration or transcription factor binding sites by correlating the sites with the diversity of available genomic annotations. Here we present Enhort, a user-friendly software tool for relating large sets of genomic positions to a variety of annotations. It functions as a statistics based genome browser, not focused on a single locus but analyzing many genomic positions simultaneously. Enhort provides comprehensive yet easy-to-use methods for statistical analysis, visualization, and the adjustment of background models according to experimental conditions and scientific questions. Enhort is publicly available online at enhort.mni.thm.de and published under GNU General Public License.

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Dinu, Valentin, Hongyu Zhao, and Perry L. Miller. "Integrating domain knowledge with statistical and data mining methods for high-density genomic SNP disease association analysis." Journal of Biomedical Informatics 40, no. 6 (December 2007): 750–60. http://dx.doi.org/10.1016/j.jbi.2007.06.002.

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ORLOV, YURIY L., RENE TE BOEKHORST, and IRINA I. ABNIZOVA. "STATISTICAL MEASURES OF THE STRUCTURE OF GENOMIC SEQUENCES: ENTROPY, COMPLEXITY, AND POSITION INFORMATION." Journal of Bioinformatics and Computational Biology 04, no. 02 (April 2006): 523–36. http://dx.doi.org/10.1142/s0219720006001801.

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Identifying regions of DNA with extreme statistical characteristics is an important aspect of the structural analysis of complete genomes. Linguistic methods, mainly based on estimating word frequency, can be used for this as they allow for the delineation of regions of low complexity. Low complexity may be due to biased nucleotide composition, by tandem- or dispersed repeats, by palindrome-hairpin structures, as well as by a combination of all these features. We developed software tools in which various numerical measures of text complexity are implemented, including combinatorial and linguistic ones. We also added Hurst exponent estimate to the software to measure dependencies in DNA sequences. By applying these tools to various functional genomic regions, we demonstrate that the complexity of introns and regulatory regions is lower than that of coding regions, whilst Hurst exponent is larger. Further analysis of promoter sequences revealed that the lower complexity of these regions is associated with long-range correlations caused by transcription factor binding sites.

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Wolf, Jason B., and Michael J. Wade. "What are maternal effects (and what are they not)?" Philosophical Transactions of the Royal Society B: Biological Sciences 364, no. 1520 (March 12, 2009): 1107–15. http://dx.doi.org/10.1098/rstb.2008.0238.

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Maternal effects can play an important role in a diversity of ecological and evolutionary processes such as population dynamics, phenotypic plasticity, niche construction, life-history evolution and the evolutionary response to selection. However, although maternal effects were defined by quantitative geneticists well over half a century ago, there remains some confusion over exactly what phenomena should be characterized as maternal effects and, more importantly, why it matters and how they are defined. We suggest a definition of maternal effects as the causal influence of the maternal genotype or phenotype on the offspring phenotype. This definition differs from some definitions in that it treats maternal effects as a phenomenon, not as a statistical construct. The causal link to maternal genotype or phenotype is the critical component of this definition providing the link between maternal effects and evolutionary and ecological processes. We show why phenomena such as maternal cytoplasmic inheritance and genomic imprinting are distinct genetically from and have different evolutionary consequences than true maternal effects. We also argue that one should consider cases where the maternal effect is conditional on offspring genotype as a class of maternal effects.

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Zhu, Jiang, Mu Su, Yue Gu, Xingda Zhang, Wenhua Lv, Shumei Zhang, Zhongyi Sun, Haibo Lu, and Yan Zhang. "Development of a method for identifying and functionally analyzing allele-specific DNA methylation based on BS-seq data." Epigenomics 11, no. 15 (November 1, 2019): 1679–92. http://dx.doi.org/10.2217/epi-2019-0023.

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Aim: To comprehensively identify allele-specific DNA methylation (ASM) at the genome-wide level. Methods: Here, we propose a new method, called GeneASM, to identify ASM using high-throughput bisulfite sequencing data in the absence of haplotype information. Results: A total of 2194 allele-specific DNA methylated genes were identified in the GM12878 lymphocyte lineage using GeneASM. These genes are mainly enriched in cell cytoplasm function, subcellular component movement or cellular linkages. GM12878 methylated DNA immunoprecipitation sequencing, and methylation sensitive restriction enzyme sequencing data were used to evaluate ASM. The relationship between ASM and disease was further analyzed using the The Cancer Genome Atlas (TCGA) data of lung adenocarcinoma (LUAD), and whole genome bisulfite sequencing data. Conclusion: GeneASM, which recognizes ASM by high-throughput bisulfite sequencing and heterozygous single-nucleotide polymorphisms, provides new perspective for studying genomic imprinting.

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Kirkpatrick, Mark, Toby Johnson, and Nick Barton. "General Models of Multilocus Evolution." Genetics 161, no. 4 (August 1, 2002): 1727–50. http://dx.doi.org/10.1093/genetics/161.4.1727.

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Abstract In 1991, Barton and Turelli developed recursions to describe the evolution of multilocus systems under arbitrary forms of selection. This article generalizes their approach to allow for arbitrary modes of inheritance, including diploidy, polyploidy, sex linkage, cytoplasmic inheritance, and genomic imprinting. The framework is also extended to allow for other deterministic evolutionary forces, including migration and mutation. Exact recursions that fully describe the state of the population are presented; these are implemented in a computer algebra package (available on the Web at http://helios.bto.ed.ac.uk/evolgen). Despite the generality of our framework, it can describe evolutionary dynamics exactly by just two equations. These recursions can be further simplified using a “quasi-linkage equilibrium” (QLE) approximation. We illustrate the methods by finding the effect of natural selection, sexual selection, mutation, and migration on the genetic composition of a population.

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Heo, J., Dong Myung Shin, Kasia Mierzejewska, Malwina Suszynska, Janina Ratajczak, Magdalena Kucia, and Mariusz Z. Ratajczak. "New Molecular Evidence That Oct-4 Is Truly Expressed In a Rare Population Of Developmental Early Stem Cells In Human Umbilical Cord Blood (UCB) and That Epigenetic Modification Of Imprinting At Igf2-H19 Locus Regulates Their Quiescent State – Potential Implications For Regenerative Medicine." Blood 122, no. 21 (November 15, 2013): 2393. http://dx.doi.org/10.1182/blood.v122.21.2393.2393.

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Abstract Background One of the most intriguing questions in stem cell biology is whether human umbilical cord blood (UCB) contains early-development stem cells that express markers of pluripotency and thus could be employed in regenerative medicine. Several groups have reported mRNAs for genes regulating stem cell pluripotency, such as Oct-4A, Nanog, and SSEA-1, in UCB cells. However, detection of the Oct-4A transcript may be hampered by the presence of several pseudogenes and Oct-4B isoform, which is not related to stem cell pluripotency. Another important question is: why are these primitive stem cells that are present in UCB highly quiescent and relatively resistant to ex vivo expansion? We previously identified a population of Oct-4+ CD133+Lin–CD45– cells in human UCB (Leukemia 2007;21:297–303) that may become specified into long-term repopulating hematopoietic stem cells (LT-HSCs) (Leukemia 2011;25:1278) and mesenchymal stem cells (Stem Cell & Dev. 2013;22:622). These Oct-4+CD133+Lin–CD45– cells present in UCB correspond to a population of murine Oct-4+Sca-1+Lin+CD45– cells that remain quiescent in bone marrow because of epigenetic modification of parentally imprinted genes, including the Igf-2-H19 tandem gene (Leukemia 2009;23:2042). The quiescence of these cells has been explained by erasure of imprinting in the regulatory differentially methylated region (DMR) at the Igf2-H19 locus. In appropriate animal models, these small cells also give rise to LT-HSCs (Exp. Hematol 2011;3:225), mesenchymal stem cells (Stem CellsDev 2010;19:1557), and lung epithelium (Stem Cells2013;doi: 10.1002/stem.1413). Moreover, as we demonstrated, the epigenetic reversal of the maternal type of imprinting to the somatic type in the DMR for the Igf2-H19 locus, which is necessary to maintain balanced expression between insulin-like growth factor 2 (Igf-2) and noncoding H19 RNA (precursor for several inhibitory miRNAs) from paternal and maternal chromosomes, respectively, is required for these cells to enter the cell cycle. The crucial role of Igf2-H19 imprinting in quiescence of the most-primitive stem cells in murine BM has been very recently confirmed by another group (Nature 2013, doi: 10.1038/nature12303). Aim of the study To address whether human UCB Oct-4± CD133±Lin–CD45– cells truly express genes regulating pluripotency, we examined the DNA methylation state of the promoters for pluripotency/germ-line genes (Oct4, Nanog, and Sall4) and of the DMR for Igf2-H19. Materials and Methods UCB CD133+Lin–CD45– cells were isolated by multiparameter fluorescence-activated cell sorting (FACS) after intra-cellular staining for Oct4 protein in lineage-depleted human UCB mononuclear cells. Bisulfide modification of DNA followed by sequencing was employed to evaluate the methylation state of CpG islands in the promoters for Oct-4, Nanog, and Sall4 as well as in the DMR for the Igf2-H19 locus. Salient Results We observed that Oct4, Nanog, and Sall4 promoters in UCB Oct-4+CD133+Lin–CD45– cells were demethylated to a similar degree as the human teratocarcinoma NTERA2, which is evidence for true expression of these genes. Furthermore, in human UCB we observed Oct-4+CD133+Lin–CD45– cells that, like their murine counterparts, erase the imprinting in the DMR at the Igf2-H19 locus, which demonstrates that genomic imprinting could be a key mechanism for maintaining the quiescence of these cells. This imprinting data was subsequently confirmed by RQ-PCR analysis of gene expression, showing downregulation of autocrine Igf-2 and upregulation of noncoding H19 RNA. Conclusion Our methylation studies of the promoters for pluripotency/germ-line genes (Oct4, Nanog, and Sall4) provide for the first time strong molecular evidence that UCB contains cells that truly express pluripotent stem cell markers. Moreover, molecular analysis of the methylation state in the DMR for the Igf2-H19 locus also explains for the first time how the quiescent state of these cells is regulated by changes in parental imprinting at the Igf2-H19 locus. Thus, elucidation of this mechanism that controls and modifies genomic imprinting in VSELs will be crucial for developing strategies to expand these cells and employ them more efficiently in regenerative medicine and we are currently working on this. Disclosures: Ratajczak: Neostem Inc.: Membership on an entity’s Board of Directors or advisory committees, Research Funding.

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von Kanel, Thomas, Dominik Gerber, André Schaller, Alessandra Baumer, Eva Wey, Christopher B. Jackson, Franziska M. Gisler, Karl Heinimann, and Sabina Gallati. "Quantitative 1-Step DNA Methylation Analysis with Native Genomic DNA as Template." Clinical Chemistry 56, no. 7 (July 1, 2010): 1098–106. http://dx.doi.org/10.1373/clinchem.2009.142828.

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Abstract Background: DNA methylation analysis currently requires complex multistep procedures based on bisulfite conversion of unmethylated cytosines or on methylation-sensitive endonucleases. To facilitate DNA methylation analysis, we have developed a quantitative 1-step assay for DNA methylation analysis. Methods: The assay is based on combining methylation-sensitive FastDigest® endonuclease digestion and quantitative real-time PCR (qPCR) in a single reaction. The first step consists of DNA digestion, followed by endonuclease inactivation and qPCR. The degree of DNA methylation is evaluated by comparing the quantification cycles of a reaction containing a methylation-sensitive endonuclease with the reaction of a sham mixture containing no endonuclease. Control reactions interrogating an unmethylated locus allow the detection and correction of artifacts caused by endonuclease inhibitors, while simultaneously permitting copy number assessment of the locus of interest. Results: With our novel approach, we correctly diagnosed the imprinting disorders Prader–Willi syndrome and Angelman syndrome in 35 individuals by measuring methylation levels and copy numbers for the SNRPN (small nuclear ribonucleoprotein polypeptide N) promoter. We also demonstrated that the proposed correction model significantly (P < 0.05) increases the assay’s accuracy with low-quality DNA, allowing analysis of DNA samples with decreased digestibility, as is often the case in retrospective studies. Conclusions: Our novel DNA methylation assay reduces both the hands-on time and errors caused by handling and pipetting and allows methylation analyses to be completed within 90 min after DNA extraction. Combined with its precision and reliability, these features make the assay well suited for diagnostic procedures as well as high-throughput analyses.

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Paim, Tiago do Prado, Patrícia Ianella, Samuel Rezende Paiva, Alexandre Rodrigues Caetano, and Concepta Margaret McManus Pimentel. "Detection and evaluation of selection signatures in sheep." Pesquisa Agropecuária Brasileira 53, no. 5 (May 2018): 527–39. http://dx.doi.org/10.1590/s0100-204x2018000500001.

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Abstract: The recent development of genome-wide single nucleotide polymorphism (SNP) arrays made it possible to carry out several studies with different species. The selection process can increase or reduce allelic (or genic) frequencies at specific loci in the genome, besides dragging neighboring alleles in the chromosome. This way, genomic regions with increased frequencies of specific alleles are formed, caracterizing selection signatures or selective sweeps. The detection of these signatures is important to characterize genetic resources, as well as to identify genes or regions involved in the control and expression of important production and economic traits. Sheep are an important species for theses studies as they are dispersed worldwide and have great phenotypic diversity. Due to the large amounts of genomic data generated, specific statistical methods and softwares are necessary for the detection of selection signatures. Therefore, the objectives of this review are to address the main statistical methods and softwares currently used for the analysis of genomic data and the identification of selection signatures; to describe the results of recent works published on selection signatures in sheep; and to discuss some challenges and opportunities in this research field.

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Cook, Kate B., Borislav H. Hristov, Karine G. Le Roch, Jean Philippe Vert, and William Stafford Noble. "Measuring significant changes in chromatin conformation with ACCOST." Nucleic Acids Research 48, no. 5 (February 8, 2020): 2303–11. http://dx.doi.org/10.1093/nar/gkaa069.

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Abstract Chromatin conformation assays such as Hi-C cannot directly measure differences in 3D architecture between cell types or cell states. For this purpose, two or more Hi-C experiments must be carried out, but direct comparison of the resulting Hi-C matrices is confounded by several features of Hi-C data. Most notably, the genomic distance effect, whereby contacts between pairs of genomic loci that are proximal along the chromosome exhibit many more Hi-C contacts that distal pairs of loci, dominates every Hi-C matrix. Furthermore, the form that this distance effect takes often varies between different Hi-C experiments, even between replicate experiments. Thus, a statistical confidence measure designed to identify differential Hi-C contacts must accurately account for the genomic distance effect or risk being misled by large-scale but artifactual differences. ACCOST (Altered Chromatin COnformation STatistics) accomplishes this goal by extending the statistical model employed by DEseq, re-purposing the ‘size factors,’ which were originally developed to account for differences in read depth between samples, to instead model the genomic distance effect. We show via analysis of simulated and real data that ACCOST provides unbiased statistical confidence estimates that compare favorably with competing methods such as diffHiC, FIND and HiCcompare. ACCOST is freely available with an Apache license at https://bitbucket.org/noblelab/accost.

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Wang, Ying, and Bruce Rannala. "Bayesian inference of fine-scale recombination rates using population genomic data." Philosophical Transactions of the Royal Society B: Biological Sciences 363, no. 1512 (October 7, 2008): 3921–30. http://dx.doi.org/10.1098/rstb.2008.0172.

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Recently, several statistical methods for estimating fine-scale recombination rates using population samples have been developed. However, currently available methods that can be applied to large-scale data are limited to approximated likelihoods. Here, we developed a full-likelihood Markov chain Monte Carlo method for estimating recombination rate under a Bayesian framework. Genealogies underlying a sampling of chromosomes are effectively modelled by using marginal individual single nucleotide polymorphism genealogies related through an ancestral recombination graph. The method is compared with two existing composite-likelihood methods using simulated data. Simulation studies show that our method performs well for different simulation scenarios. The method is applied to two human population genetic variation datasets that have been studied by sperm typing. Our results are consistent with the estimates from sperm crossover analysis.

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Journal articles on the topic 'Genomic imprinting - Statistical methods'

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