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Analysis of Genetic Interactions Involving Maternal and Offspring Genotypes at Different Loci: Power Simulation and Application to Testicular Cancer
The analyses of genetic interaction between maternal and offspring genotypes are usually conducted considering a single locus. Here, we propose testing maternal × offspring (M×O) and maternal × maternal (M×M) genotype interactions involving two unlinked loci. We reformulate the log-linear approach o...
Autores principales: | , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
Wiley Subscription Services, Inc., A Wiley Company
2012
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC3504980/ https://www.ncbi.nlm.nih.gov/pubmed/22740241 http://dx.doi.org/10.1002/gepi.21655 |
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author | Nsengimana, Jérémie Barrett, Jennifer H |
author_facet | Nsengimana, Jérémie Barrett, Jennifer H |
author_sort | Nsengimana, Jérémie |
collection | PubMed |
description | The analyses of genetic interaction between maternal and offspring genotypes are usually conducted considering a single locus. Here, we propose testing maternal × offspring (M×O) and maternal × maternal (M×M) genotype interactions involving two unlinked loci. We reformulate the log-linear approach of analyzing cases and their parents (family trios) to accommodate two loci, fit fuller models to avoid confounding in a first analysis step and propose that the model be reduced to the most prominent effects in a second step. We conduct extensive simulations to assess the validity and power of this approach under various model assumptions. We show that the approach is valid and has good power to detect M×O and M×M interactions. For example, the power to detect a dominant interaction relative risk of 1.5 (both M×O and M×M) is 70% with 300 trios and approaches 100% with 1,000 trios. Unlike the main effects, M×O and M×M interactions are conditionally independent of mating types, and consequently, their power is not affected by missing paternal genotypes. When applied to single-locus M×O interaction, our method is as powerful as other existing methods. Applying the method to testicular cancer, we found a nominally significant M×M interaction between single nucleotide polymorphisms from C-Kit Ligand (KITLG) and Sex Hormone Binding Globulin (SHBG) using 210 families (relative risk 2.2, P = 0.03). This finding supports a role of maternal hormones in offspring testicular cancer and warrants confirmation in a larger dataset. |
format | Online Article Text |
id | pubmed-3504980 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2012 |
publisher | Wiley Subscription Services, Inc., A Wiley Company |
record_format | MEDLINE/PubMed |
spelling | pubmed-35049802012-11-30 Analysis of Genetic Interactions Involving Maternal and Offspring Genotypes at Different Loci: Power Simulation and Application to Testicular Cancer Nsengimana, Jérémie Barrett, Jennifer H Genet Epidemiol Research Articles The analyses of genetic interaction between maternal and offspring genotypes are usually conducted considering a single locus. Here, we propose testing maternal × offspring (M×O) and maternal × maternal (M×M) genotype interactions involving two unlinked loci. We reformulate the log-linear approach of analyzing cases and their parents (family trios) to accommodate two loci, fit fuller models to avoid confounding in a first analysis step and propose that the model be reduced to the most prominent effects in a second step. We conduct extensive simulations to assess the validity and power of this approach under various model assumptions. We show that the approach is valid and has good power to detect M×O and M×M interactions. For example, the power to detect a dominant interaction relative risk of 1.5 (both M×O and M×M) is 70% with 300 trios and approaches 100% with 1,000 trios. Unlike the main effects, M×O and M×M interactions are conditionally independent of mating types, and consequently, their power is not affected by missing paternal genotypes. When applied to single-locus M×O interaction, our method is as powerful as other existing methods. Applying the method to testicular cancer, we found a nominally significant M×M interaction between single nucleotide polymorphisms from C-Kit Ligand (KITLG) and Sex Hormone Binding Globulin (SHBG) using 210 families (relative risk 2.2, P = 0.03). This finding supports a role of maternal hormones in offspring testicular cancer and warrants confirmation in a larger dataset. Wiley Subscription Services, Inc., A Wiley Company 2012-09 2012-06 /pmc/articles/PMC3504980/ /pubmed/22740241 http://dx.doi.org/10.1002/gepi.21655 Text en © 2012 Wiley Periodicals, Inc. http://creativecommons.org/licenses/by/2.5/ Re-use of this article is permitted in accordance with the Creative Commons Deed, Attribution 2.5, which does not permit commercial exploitation. |
spellingShingle | Research Articles Nsengimana, Jérémie Barrett, Jennifer H Analysis of Genetic Interactions Involving Maternal and Offspring Genotypes at Different Loci: Power Simulation and Application to Testicular Cancer |
title | Analysis of Genetic Interactions Involving Maternal and Offspring Genotypes at Different Loci: Power Simulation and Application to Testicular Cancer |
title_full | Analysis of Genetic Interactions Involving Maternal and Offspring Genotypes at Different Loci: Power Simulation and Application to Testicular Cancer |
title_fullStr | Analysis of Genetic Interactions Involving Maternal and Offspring Genotypes at Different Loci: Power Simulation and Application to Testicular Cancer |
title_full_unstemmed | Analysis of Genetic Interactions Involving Maternal and Offspring Genotypes at Different Loci: Power Simulation and Application to Testicular Cancer |
title_short | Analysis of Genetic Interactions Involving Maternal and Offspring Genotypes at Different Loci: Power Simulation and Application to Testicular Cancer |
title_sort | analysis of genetic interactions involving maternal and offspring genotypes at different loci: power simulation and application to testicular cancer |
topic | Research Articles |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC3504980/ https://www.ncbi.nlm.nih.gov/pubmed/22740241 http://dx.doi.org/10.1002/gepi.21655 |
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