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Impact of Zygosity on Bimodal Phenotype Distributions.

ABSTRACT: Allele number, or zygosity, is a clear determinant of gene expression in diploid cells. However, the relationship between the number of copies of a gene and its expression can be hard to anticipate, especially when the gene in question is embedded in a regulatory circuit that contains feedback. Here, we study this question making use of the natural genetic variability of human populations, which allows us to compare the expression profiles of a receptor protein in natural killer cells among donors infected with human cytomegalovirus with one or two copies of the allele. Crucially, the distribution of gene expression in many of the donors is bimodal, which indicates the presence of a positive feedback loop somewhere in the regulatory environment of the gene. Three separate gene-circuit models differing in the location of the positive feedback loop with respect to the gene can all reproduce the homozygous data. However, when the resulting fitted models are applied to the hemizygous donors, one model (the one with the positive feedback located at the level of gene transcription) is superior in describing the experimentally observed gene-expression profile. In that way, our work shows that zygosity can help us relate the structure and function of gene regulatory networks.

SUBMITTER: Holst-Hansen T

PROVIDER: S-EPMC5510722 | biostudies-literature | 2017 Jul

REPOSITORIES: biostudies-literature

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Impact of Zygosity on Bimodal Phenotype Distributions.

Holst-Hansen Thomas T Abad Elena E Muntasell Aura A López-Botet Miguel M Jensen Mogens H MH Trusina Ala A Garcia-Ojalvo Jordi J

Biophysical journal 20170701 1

Allele number, or zygosity, is a clear determinant of gene expression in diploid cells. However, the relationship between the number of copies of a gene and its expression can be hard to anticipate, especially when the gene in question is embedded in a regulatory circuit that contains feedback. Here, we study this question making use of the natural genetic variability of human populations, which allows us to compare the expression profiles of a receptor protein in natural killer cells among dono ...[more]

PMID: 28700913

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Project description:BackgroundCurrently very few noninvasive molecular genetic approaches are available to determine zygosity for twin pregnancies in clinical laboratories. This study aimed to develop a novel method to determine zygosity by using maternal plasma target region sequencing.MethodsWe constructed a statistic model to calculate the possibility of each zygosity type using likelihood ratios ( Li ) and empirical dynamic thresholds targeting at 4,524 single nucleotide polymorphisms (SNPs) loci on 22 autosomes. Then two dizygotic (DZ) twin pregnancies,two monozygotic (MZ) twin pregnancies and two singletons were recruited to evaluate the performance of our novel method. Finally we estimated the sensitivity and specificity of the model in silico under different cell-free fetal DNA (cff-DNA) concentration and sequence depth.Results/conclusionsWe obtained 8.90 Gbp sequencing data on average for six clinical samples. Two samples were classified as DZ with L values of 1.891 and 1.554, higher than the dynamic DZ cut-off values of 1.162 and 1.172, respectively. Another two samples were judged as MZ with 0.763 and 0.784 of L values, lower than the MZ cut-off values of 0.903 and 0.918. And the rest two singleton samples were regarded as MZ twins, with L values of 0.639 and 0.757, lower than the MZ cut-off values of 0.921 and 0.799. In silico, the estimated sensitivity of our noninvasive zygosity determination was 99.90% under 10% total cff-DNA concentration with 2 Gbp sequence data. As the cff-DNA concentration increased to 15%, the specificity was as high as 97% with 3.50 Gbp sequence data, much higher than 80% with 10% cff-DNA concentration.SignificanceThis study presents the feasibility to noninvasively determine zygosity of twin pregnancy using target region sequencing, and illustrates the sensitivity and specificity under various detecting condition. Our method can act as an alternative approach for zygosity determination of twin pregnancies in clinical practice.

Dataset Information

Impact of Zygosity on Bimodal Phenotype Distributions.

Publications

Impact of Zygosity on Bimodal Phenotype Distributions.

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