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Fetal de novo mutations and preterm birth

View Article: PubMed Central - PubMed

ABSTRACT

Preterm birth (PTB) affects ~12% of pregnancies in the US. Despite its high mortality and morbidity, the molecular etiology underlying PTB has been unclear. Numerous studies have been devoted to identifying genetic factors in maternal and fetal genomes, but so far few genomic loci have been associated with PTB. By analyzing whole-genome sequencing data from 816 trio families, for the first time, we observed the role of fetal de novo mutations in PTB. We observed a significant increase in de novo mutation burden in PTB fetal genomes. Our genomic analyses further revealed that affected genes by PTB de novo mutations were dosage sensitive, intolerant to genomic deletions, and their mouse orthologs were likely developmentally essential. These genes were significantly involved in early fetal brain development, which was further supported by our analysis of copy number variants identified from an independent PTB cohort. Our study indicates a new mechanism in PTB occurrence independently contributed from fetal genomes, and thus opens a new avenue for future PTB research.

No MeSH data available.


Significantly increased de novo mutation burden in preterm newborn’s genomes.The distribution of the number of de novo mutations per genome was compared between the PTB and non-PTB cohorts (P = 6.9e-3), and statistical significance was determined by Wilcoxon rank-sum test. Kernel density estimation was used to derive the probability density functions.
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pgen.1006689.g002: Significantly increased de novo mutation burden in preterm newborn’s genomes.The distribution of the number of de novo mutations per genome was compared between the PTB and non-PTB cohorts (P = 6.9e-3), and statistical significance was determined by Wilcoxon rank-sum test. Kernel density estimation was used to derive the probability density functions.

Mentions: Because the amount of de novo mutations in personal genomes is strongly scaled by paternal age[19–21], and is modestly (or weakly) correlated with maternal age[20, 23], we first examined the parental age distribution, and found that the paternal and maternal age distributions were similar between the PTB and term infants (paternal age for PTB was 33.9±6.1 and for non-PTB was 33.5±5.8, P = 0.25, Wilcoxon rank-sum test; maternal age for PTB was 31.7±5.1 and for non-PTB was 31.4±4.9, P = 0.31, Wilcoxon rank-sum test, S2 Table). Finding insufficient evidence that parental ages were potential confounders, we compared the number of de novo mutations in each infant genome, and observed a significant increase in the de novo mutation burden in PTB infants relative to term infants (Fig 2, P = 6.9e-3, Wilcoxon rank-sum test, S3 Table). Notably, by identifying individuals with extreme de novo mutation load (the top 5% across all 816 subjects), we did not observe a statistical difference in paternal age between PTB and term groups (P = 0.62, Wilcoxon rank-sum test), nor in maternal age (P = 0.53, Wilcoxon rank-sum test). We performed two additional tests to ensure that the increased de novo mutation load in PTB cases was not resultant from unequal parental age distribution in this group. First, we performed logistic regression to combinatorially model paternal age, maternal age and the number of de novo mutations in each infant genome, which served to assess their individual effects on predicting the binary preterm status (as the response variable in the logistic model, Methods and Materials). Only the regression term for de novo mutation load exhibited a significant statistical association with preterm status (regression coefficient was 0.27, P = 4.1e-3), and the terms for parental ages did not (P>0.5, S3 Table). Second, we observed that Pearson’s correlation between paternal age and de novo mutation load across the 816 trios was 0.62, suggesting that ~38% (R2) of the variability in de novo mutation load could be explained by paternal age differences. Therefore, we fit the de novo mutation counts (the response variable) with the paternal ages (the explanatory variable) across the 816 family trios, and only considered the residuals of the de novo mutation count after subtracting the effect from paternal age. Again, the corrected de novo mutation counts (the residuals) consistently exhibited a significant increase in the PTB group relative to the term group (P = 6.3e-3. Wilcoxon rank-sum test). Similar analysis was also performed on maternal age, and confirmed the same observation (P = 8.7e-3, Wilcoxon rank-sum test).


Fetal de novo mutations and preterm birth
Significantly increased de novo mutation burden in preterm newborn’s genomes.The distribution of the number of de novo mutations per genome was compared between the PTB and non-PTB cohorts (P = 6.9e-3), and statistical significance was determined by Wilcoxon rank-sum test. Kernel density estimation was used to derive the probability density functions.
© Copyright Policy
Related In: Results  -  Collection

License
Show All Figures
getmorefigures.php?uid=PMC5384656&req=5

pgen.1006689.g002: Significantly increased de novo mutation burden in preterm newborn’s genomes.The distribution of the number of de novo mutations per genome was compared between the PTB and non-PTB cohorts (P = 6.9e-3), and statistical significance was determined by Wilcoxon rank-sum test. Kernel density estimation was used to derive the probability density functions.
Mentions: Because the amount of de novo mutations in personal genomes is strongly scaled by paternal age[19–21], and is modestly (or weakly) correlated with maternal age[20, 23], we first examined the parental age distribution, and found that the paternal and maternal age distributions were similar between the PTB and term infants (paternal age for PTB was 33.9±6.1 and for non-PTB was 33.5±5.8, P = 0.25, Wilcoxon rank-sum test; maternal age for PTB was 31.7±5.1 and for non-PTB was 31.4±4.9, P = 0.31, Wilcoxon rank-sum test, S2 Table). Finding insufficient evidence that parental ages were potential confounders, we compared the number of de novo mutations in each infant genome, and observed a significant increase in the de novo mutation burden in PTB infants relative to term infants (Fig 2, P = 6.9e-3, Wilcoxon rank-sum test, S3 Table). Notably, by identifying individuals with extreme de novo mutation load (the top 5% across all 816 subjects), we did not observe a statistical difference in paternal age between PTB and term groups (P = 0.62, Wilcoxon rank-sum test), nor in maternal age (P = 0.53, Wilcoxon rank-sum test). We performed two additional tests to ensure that the increased de novo mutation load in PTB cases was not resultant from unequal parental age distribution in this group. First, we performed logistic regression to combinatorially model paternal age, maternal age and the number of de novo mutations in each infant genome, which served to assess their individual effects on predicting the binary preterm status (as the response variable in the logistic model, Methods and Materials). Only the regression term for de novo mutation load exhibited a significant statistical association with preterm status (regression coefficient was 0.27, P = 4.1e-3), and the terms for parental ages did not (P>0.5, S3 Table). Second, we observed that Pearson’s correlation between paternal age and de novo mutation load across the 816 trios was 0.62, suggesting that ~38% (R2) of the variability in de novo mutation load could be explained by paternal age differences. Therefore, we fit the de novo mutation counts (the response variable) with the paternal ages (the explanatory variable) across the 816 family trios, and only considered the residuals of the de novo mutation count after subtracting the effect from paternal age. Again, the corrected de novo mutation counts (the residuals) consistently exhibited a significant increase in the PTB group relative to the term group (P = 6.3e-3. Wilcoxon rank-sum test). Similar analysis was also performed on maternal age, and confirmed the same observation (P = 8.7e-3, Wilcoxon rank-sum test).

View Article: PubMed Central - PubMed

ABSTRACT

Preterm birth (PTB) affects ~12% of pregnancies in the US. Despite its high mortality and morbidity, the molecular etiology underlying PTB has been unclear. Numerous studies have been devoted to identifying genetic factors in maternal and fetal genomes, but so far few genomic loci have been associated with PTB. By analyzing whole-genome sequencing data from 816 trio families, for the first time, we observed the role of fetal de novo mutations in PTB. We observed a significant increase in de novo mutation burden in PTB fetal genomes. Our genomic analyses further revealed that affected genes by PTB de novo mutations were dosage sensitive, intolerant to genomic deletions, and their mouse orthologs were likely developmentally essential. These genes were significantly involved in early fetal brain development, which was further supported by our analysis of copy number variants identified from an independent PTB cohort. Our study indicates a new mechanism in PTB occurrence independently contributed from fetal genomes, and thus opens a new avenue for future PTB research.

No MeSH data available.