Dataset Information

Autozygosity mapping and time-to-spontaneous delivery in Norwegian parent-offspring trios.

ABSTRACT: Parental genetic relatedness may lead to adverse health and fitness outcomes in the offspring. However, the degree to which it affects human delivery timing is unknown. We use genotype data from ?25?000 parent-offspring trios from the Norwegian Mother, Father and Child Cohort Study to optimize runs of homozygosity (ROH) calling by maximising the correlation between parental genetic relatedness and offspring ROHs. We then estimate the effect of maternal, paternal, and fetal autozygosity and that of autozygosity mapping (common segments and gene burden test) on the timing of spontaneous onset of delivery. The correlation between offspring ROH using a variety of parameters and parental genetic relatedness ranged between -0.2 and 0.6, revealing the importance of the minimum number of genetic variants included in a ROH and the use of genetic distance. The optimized compared to predefined parameters showed a ?45% higher correlation between parental genetic relatedness and offspring ROH. We found no evidence of an effect of maternal, paternal nor fetal overall autozygosity on spontaneous delivery timing. Yet, through autozygosity mapping, we identified three maternal loci TBC1D1, SIGLECs and EDN1 gene regions reducing median time-to-spontaneous onset of delivery by ?2-5% (P-value< 2.3 × 10-6). We also found suggestive evidence of a fetal locus at 3q22.2, near the RYK gene region (P-value= 2.0 × 10-6). Autozygosity mapping may provide new insights on the genetic determinants of delivery timing beyond traditional genome-wide association studies, but particular and rigorous attention should be given to ROH calling parameter selection.

SUBMITTER: Sole-Navais P

PROVIDER: S-EPMC7861013 | biostudies-literature | 2020 Dec

REPOSITORIES: biostudies-literature

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Autozygosity mapping and time-to-spontaneous delivery in Norwegian parent-offspring trios.

Sole-Navais Pol P Bacelis Jonas J Helgeland Øyvind Ø Modzelewska Dominika D Vaudel Marc M Flatley Christopher C Andreassen Ole O Njølstad Pål R PR Muglia Louis J LJ Johansson Stefan S Zhang Ge G Jacobsson Bo B

Human molecular genetics 20210201 23

Parental genetic relatedness may lead to adverse health and fitness outcomes in the offspring. However, the degree to which it affects human delivery timing is unknown. We use genotype data from ≃25 000 parent-offspring trios from the Norwegian Mother, Father and Child Cohort Study to optimize runs of homozygosity (ROH) calling by maximizing the correlation between parental genetic relatedness and offspring ROHs. We then estimate the effect of maternal, paternal and fetal autozygosity and that o ...[more]

PMID: 33291140

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Project description:MotivationWhole-genome and -exome sequencing on parent-offspring trios is a powerful approach to identifying disease-associated genes by detecting de novo mutations in patients. Accurate detection of de novo mutations from sequencing data is a critical step in trio-based genetic studies. Existing bioinformatic approaches usually yield high error rates due to sequencing artifacts and alignment issues, which may either miss true de novo mutations or call too many false ones, making downstream validation and analysis difficult. In particular, current approaches have much worse specificity than sensitivity, and developing effective filters to discriminate genuine from spurious de novo mutations remains an unsolved challenge.ResultsIn this article, we curated 59 sequence features in whole genome and exome alignment context which are considered to be relevant to discriminating true de novo mutations from artifacts, and then employed a machine-learning approach to classify candidates as true or false de novo mutations. Specifically, we built a classifier, named De Novo Mutation Filter (DNMFilter), using gradient boosting as the classification algorithm. We built the training set using experimentally validated true and false de novo mutations as well as collected false de novo mutations from an in-house large-scale exome-sequencing project. We evaluated DNMFilter's theoretical performance and investigated relative importance of different sequence features on the classification accuracy. Finally, we applied DNMFilter on our in-house whole exome trios and one CEU trio from the 1000 Genomes Project and found that DNMFilter could be coupled with commonly used de novo mutation detection approaches as an effective filtering approach to significantly reduce false discovery rate without sacrificing sensitivity.AvailabilityThe software DNMFilter implemented using a combination of Java and R is freely available from the website at http://humangenome.duke.edu/software.

Project description:BackgroundMany studies detect associations between parent behaviour and child symptoms of anxiety and depression. Despite knowledge that anxiety and depression are influenced by a complex interplay of genetic and environmental risk factors, most studies do not account for shared familial genetic risk. Quantitative genetic designs provide a means of controlling for shared genetics, but rely on observed putative exposure variables, and require data from highly specific family structures.MethodsThe intergenerational genomic method, Relatedness Disequilibrium Regression (RDR), indexes environmental effects of parents on child traits using measured genotypes. RDR estimates how much the parent genome influences the child indirectly via the environment, over and above effects of genetic factors acting directly in the child. This 'genetic nurture' effect is agnostic to parent phenotype and captures unmeasured heritable parent behaviours. We applied RDR in a sample of 11,598 parent-offspring trios from the Norwegian Mother, Father and Child Cohort Study (MoBa) to estimate parental genetic nurture separately from direct child genetic effects on anxiety and depression symptoms at age 8. We tested for mediation of genetic nurture via maternal anxiety and depression symptoms. Results were compared to a complementary non-genomic pedigree model.ResultsParental genetic nurture explained 14% of the variance in depression symptoms at age 8. Subsequent analyses suggested that maternal anxiety and depression partially mediated this effect. The genetic nurture effect was mirrored by the finding of family environmental influence in our pedigree model. In contrast, variance in anxiety symptoms was not significantly influenced by common genetic variation in children or parents, despite a moderate pedigree heritability.ConclusionsGenomic methods like RDR represent new opportunities for genetically sensitive family research on complex human traits, which until now has been largely confined to adoption, twin and other pedigree designs. Our results are relevant to debates about the role of parents in the development of anxiety and depression in children, and possibly where to intervene to reduce problems.

Project description:ObjectiveMetabolic syndrome (MS) and its components are observed to emerge in childhood and may continue into adulthood. The study aimed to investigate the association between parental overweight and risk of childhood MS and its components in their offspring.MethodsData were obtained from a cross-sectional survey conducted in Chinese children and adolescents; a total of 11 784 children aged 7-18 years were included in this study; child outcomes were obtained from objective measurements and parental data were obtained from questionnaires; MS was defined according to the modified criteria of Adult Treatment Panel Ⅲ; correlation between parental overweight and offspring MS was assessed via multivariate logistic regression models adjusted for potential covariates.Results3476 (29.5%) children were found to have overweight fathers, 1041 (8.8%) had overweight mothers and 852 (7.2%) had both overweight parents. The prevalence of MS was 7.1% in total, 8.2% in boys and 5.9% in girls; children with overweight parents had a higher prevalence of MS and its components (except for elevated glucose) compared with children with normal-weight parents. Children with overweight fathers, mothers and both parents had 2.17 times (95% CI: 1.65-2.85), 2.89 times (95% CI: 2.03-4.11) and 2.81 times (95% CI: 1.91-4.15) higher risk of MS, respectively. Children with overweight mothers were likely to have a higher risk of MS compared with children with overweight fathers. Parental overweight was positively correlated with higher risk of MS, abdominal obesity and low HDL-C both in boys and girls.ConclusionParental overweight was strongly associated with increased risk of MS in their offspring, the risk was highest in children with both parents to be overweight. Maternal overweight seems to have a stronger correlation with offspring MS than paternal overweight. Parental overweight is one of the factors for identifying metabolic dysfunction risk in their offspring and other factors need to be considered as well.

Dataset Information

Autozygosity mapping and time-to-spontaneous delivery in Norwegian parent-offspring trios.

Publications

Autozygosity mapping and time-to-spontaneous delivery in Norwegian parent-offspring trios.

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