EconPapers    
Economics at your fingertips  
 

Properties and Evaluation of the MOBIT – a novel Linkage-based Test Statistic and Quantification Method for Imprinting

Brugger Markus (), Strauch Konstantin and Knapp Michael
Additional contact information
Strauch Konstantin: Chair of Genetic Epidemiology, IBE, Faculty of Medicine, LMU Munich, Munich, Germany
Knapp Michael: Institute for Medical Biometry, Informatics and Epidemiology, University of Bonn, Venusberg-Campus 1, DE-53127 Bonn, Germany

Statistical Applications in Genetics and Molecular Biology, 2019, vol. 18, issue 4, 40

Abstract: Genomic imprinting is a parent-of-origin effect apparent in an appreciable number of human diseases. We have proposed the new imprinting test statistic MOBIT, which is based on MOD score analysis. We were interested in the properties of the MOBIT concerning its distribution under three hypotheses: (1) H0,a: no linkage, no imprinting; (2) H0,b: linkage, no imprinting; (3) H1: linkage and imprinting. More specifically, we assessed the confounding between imprinting and sex-specific recombination frequencies, which presents a major difficulty in linkage-based testing for imprinting, and evaluated the power of the test. To this end, we have performed a linkage simulation study of affected sib-pairs and a three-generation pedigree with two trait models, many two- and multipoint marker scenarios, three genetic map ratios, two sample sizes, and five imprinting degrees. We also investigated the ability of the MOBIT to quantify the degree of imprinting and applied the MOBIT using a real data example on house dust mite allergy. We further proposed and evaluated two approaches to obtain empiric p values for the MOBIT. Our results showed that twopoint analyses assuming a sex-averaged marker map led to an inflated type I error due to confounding, especially for a larger marker-trait locus distance. When the correct sex-specific marker map was assumed, twopoint analyses have a reduced power to detect imprinting, compared to sex-averaged analyses with an appropriate correction for the inflation of the test statistic. However, confounding was not an issue in multipoint analysis unless the map ratio was extreme and marker spacing was sparse. With multipoint analysis, power as well as the ability to quantify the imprinting degree were almost equally high when a sex-averaged or the correct sex-specific map was used in the analysis. We recommend to obtain empiric p values for the MOBIT using genotype simulations based on the best-fitting nonimprinting model of the real dataset analysis. In addition, an implementation of a method based on the permutation of parental sexes is also available. In summary, we propose to perform multipoint analyses using densely spaced markers to efficiently discover new imprinted loci and to reliably quantify the degree of imprinting.

Keywords: confounding; genomic imprinting; linkage analysis; MOD scores; sex-specific recombination frequencies (search for similar items in EconPapers)
Date: 2019
References: Add references at CitEc
Citations: Track citations by RSS feed

Downloads: (external link)
https://doi.org/10.1515/sagmb-2018-0025 (text/html)
For access to full text, subscription to the journal or payment for the individual article is required.

Related works:
This item may be available elsewhere in EconPapers: Search for items with the same title.

Export reference: BibTeX RIS (EndNote, ProCite, RefMan) HTML/Text

Persistent link: https://EconPapers.repec.org/RePEc:bpj:sagmbi:v:18:y:2019:i:4:p:40:n:4

Ordering information: This journal article can be ordered from
https://www.degruyter.com/view/j/sagmb

DOI: 10.1515/sagmb-2018-0025

Access Statistics for this article

Statistical Applications in Genetics and Molecular Biology is currently edited by Michael P. H. Stumpf

More articles in Statistical Applications in Genetics and Molecular Biology from De Gruyter
Bibliographic data for series maintained by Peter Golla ().

 
Page updated 2021-05-24
Handle: RePEc:bpj:sagmbi:v:18:y:2019:i:4:p:40:n:4