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A Computational Approach to Estimating Nondisjunction Frequency in Saccharomyces cerevisiae
Errors segregating homologous chromosomes during meiosis result in aneuploid gametes and are the largest contributing factor to birth defects and spontaneous abortions in humans. Saccharomyces cerevisiae has long served as a model organism for studying the gene network supporting normal chromosome s...
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Formato: | Online Artículo Texto |
Lenguaje: | English |
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Genetics Society of America
2016
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC4777129/ https://www.ncbi.nlm.nih.gov/pubmed/26747203 http://dx.doi.org/10.1534/g3.115.024380 |
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author | Chu, Daniel B. Burgess, Sean M. |
author_facet | Chu, Daniel B. Burgess, Sean M. |
author_sort | Chu, Daniel B. |
collection | PubMed |
description | Errors segregating homologous chromosomes during meiosis result in aneuploid gametes and are the largest contributing factor to birth defects and spontaneous abortions in humans. Saccharomyces cerevisiae has long served as a model organism for studying the gene network supporting normal chromosome segregation. Measuring homolog nondisjunction frequencies is laborious, and involves dissecting thousands of tetrads to detect missegregation of individually marked chromosomes. Here we describe a computational method (TetFit) to estimate the relative contributions of meiosis I nondisjunction and random-spore death to spore inviability in wild type and mutant strains. These values are based on finding the best-fit distribution of 4, 3, 2, 1, and 0 viable-spore tetrads to an observed distribution. Using TetFit, we found that meiosis I nondisjunction is an intrinsic component of spore inviability in wild-type strains. We show proof-of-principle that the calculated average meiosis I nondisjunction frequency determined by TetFit closely matches empirically determined values in mutant strains. Using these published data sets, TetFit uncovered two classes of mutants: Class A mutants skew toward increased nondisjunction death, and include those with known defects in establishing pairing, recombination, and/or synapsis of homologous chromosomes. Class B mutants skew toward random spore death, and include those with defects in sister-chromatid cohesion and centromere function. Epistasis analysis using TetFit is facilitated by the low numbers of tetrads (as few as 200) required to compare the contributions to spore death in different mutant backgrounds. TetFit analysis does not require any special strain construction, and can be applied to previously observed tetrad distributions. |
format | Online Article Text |
id | pubmed-4777129 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2016 |
publisher | Genetics Society of America |
record_format | MEDLINE/PubMed |
spelling | pubmed-47771292016-03-03 A Computational Approach to Estimating Nondisjunction Frequency in Saccharomyces cerevisiae Chu, Daniel B. Burgess, Sean M. G3 (Bethesda) Investigations Errors segregating homologous chromosomes during meiosis result in aneuploid gametes and are the largest contributing factor to birth defects and spontaneous abortions in humans. Saccharomyces cerevisiae has long served as a model organism for studying the gene network supporting normal chromosome segregation. Measuring homolog nondisjunction frequencies is laborious, and involves dissecting thousands of tetrads to detect missegregation of individually marked chromosomes. Here we describe a computational method (TetFit) to estimate the relative contributions of meiosis I nondisjunction and random-spore death to spore inviability in wild type and mutant strains. These values are based on finding the best-fit distribution of 4, 3, 2, 1, and 0 viable-spore tetrads to an observed distribution. Using TetFit, we found that meiosis I nondisjunction is an intrinsic component of spore inviability in wild-type strains. We show proof-of-principle that the calculated average meiosis I nondisjunction frequency determined by TetFit closely matches empirically determined values in mutant strains. Using these published data sets, TetFit uncovered two classes of mutants: Class A mutants skew toward increased nondisjunction death, and include those with known defects in establishing pairing, recombination, and/or synapsis of homologous chromosomes. Class B mutants skew toward random spore death, and include those with defects in sister-chromatid cohesion and centromere function. Epistasis analysis using TetFit is facilitated by the low numbers of tetrads (as few as 200) required to compare the contributions to spore death in different mutant backgrounds. TetFit analysis does not require any special strain construction, and can be applied to previously observed tetrad distributions. Genetics Society of America 2016-01-08 /pmc/articles/PMC4777129/ /pubmed/26747203 http://dx.doi.org/10.1534/g3.115.024380 Text en Copyright © 2016 Chu and Burgess http://creativecommons.org/licenses/by/4.0/ This is an open-access article distributed under the terms of the Creative Commons Attribution 4.0 International License (http://creativecommons.org/licenses/by/4.0/), which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited. |
spellingShingle | Investigations Chu, Daniel B. Burgess, Sean M. A Computational Approach to Estimating Nondisjunction Frequency in Saccharomyces cerevisiae |
title | A Computational Approach to Estimating Nondisjunction Frequency in Saccharomyces cerevisiae |
title_full | A Computational Approach to Estimating Nondisjunction Frequency in Saccharomyces cerevisiae |
title_fullStr | A Computational Approach to Estimating Nondisjunction Frequency in Saccharomyces cerevisiae |
title_full_unstemmed | A Computational Approach to Estimating Nondisjunction Frequency in Saccharomyces cerevisiae |
title_short | A Computational Approach to Estimating Nondisjunction Frequency in Saccharomyces cerevisiae |
title_sort | computational approach to estimating nondisjunction frequency in saccharomyces cerevisiae |
topic | Investigations |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC4777129/ https://www.ncbi.nlm.nih.gov/pubmed/26747203 http://dx.doi.org/10.1534/g3.115.024380 |
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