Limits...
Targeted Sequencing and Meta-Analysis of Preterm Birth.

Uzun A, Schuster J, McGonnigal B, Schorl C, Dewan A, Padbury J - PLoS ONE (2016)

Bottom Line: We compared variants identified by targeted sequencing of women with 2-3 generations of preterm birth with term controls without history of preterm birth.Additionally, SERPINB8, AZU1 and WASF3 showed significant differences in abundance of variants in the univariate comparison of cases and controls.The biological processes impacted by these gene sets included: cell motility, migration and locomotion; response to glucocorticoid stimulus; signal transduction; metabolic regulation and control of apoptosis.

View Article: PubMed Central - PubMed

Affiliation: Department of Pediatrics, Women & Infants Hospital of Rhode Island, Providence, Rhode Island, United States of America.

ABSTRACT
Understanding the genetic contribution(s) to the risk of preterm birth may lead to the development of interventions for treatment, prediction and prevention. Twin studies suggest heritability of preterm birth is 36-40%. Large epidemiological analyses support a primary maternal origin for recurrence of preterm birth, with little effect of paternal or fetal genetic factors. We exploited an "extreme phenotype" of preterm birth to leverage the likelihood of genetic discovery. We compared variants identified by targeted sequencing of women with 2-3 generations of preterm birth with term controls without history of preterm birth. We used a meta-genomic, bi-clustering algorithm to identify gene sets coordinately associated with preterm birth. We identified 33 genes including 217 variants from 5 modules that were significantly different between cases and controls. The most frequently identified and connected genes in the exome library were IGF1, ATM and IQGAP2. Likewise, SOS1, RAF1 and AKT3 were most frequent in the haplotype library. Additionally, SERPINB8, AZU1 and WASF3 showed significant differences in abundance of variants in the univariate comparison of cases and controls. The biological processes impacted by these gene sets included: cell motility, migration and locomotion; response to glucocorticoid stimulus; signal transduction; metabolic regulation and control of apoptosis.

No MeSH data available.


Related in: MedlinePlus

Meta-analysis and analytical pipeline:The genes harboring variants in each patient were analyzed by gene set enrichment using the MSig database C2 collection of gene sets [43]. The significant gene sets for each patient were combined into a binary association matrix. The iBBiG algorithm extracts modules of gene sets and patient subsets from the data matrix. The modules are represented by different colors. Fisher’s exact test was used to identify modules with significant differences in the number of cases and controls.
© Copyright Policy
Related In: Results  -  Collection

License
getmorefigures.php?uid=PMC4862658&req=5

pone.0155021.g002: Meta-analysis and analytical pipeline:The genes harboring variants in each patient were analyzed by gene set enrichment using the MSig database C2 collection of gene sets [43]. The significant gene sets for each patient were combined into a binary association matrix. The iBBiG algorithm extracts modules of gene sets and patient subsets from the data matrix. The modules are represented by different colors. Fisher’s exact test was used to identify modules with significant differences in the number of cases and controls.

Mentions: The genes containing variants that showed significant differences between cases and controls were examined for their association with networks and biological pathways using GSEA. We analyzed genes whose variants differed from controls with a p-value <0.1. We ran GSEA independently for each of the 48 patients. The significant gene sets from the GSEA of each patient were then compared by adapting a newly described meta-analytic approach known as iterative binary bi-clustering (iBBiG) [42]. The iBBiG algorithm identifies “modules” of gene sets and patient subsets from binary data [42]. Our analytical pipeline is illustrated in Fig 2.


Targeted Sequencing and Meta-Analysis of Preterm Birth.

Uzun A, Schuster J, McGonnigal B, Schorl C, Dewan A, Padbury J - PLoS ONE (2016)

Meta-analysis and analytical pipeline:The genes harboring variants in each patient were analyzed by gene set enrichment using the MSig database C2 collection of gene sets [43]. The significant gene sets for each patient were combined into a binary association matrix. The iBBiG algorithm extracts modules of gene sets and patient subsets from the data matrix. The modules are represented by different colors. Fisher’s exact test was used to identify modules with significant differences in the number of cases and controls.
© Copyright Policy
Related In: Results  -  Collection

License
Show All Figures
getmorefigures.php?uid=PMC4862658&req=5

pone.0155021.g002: Meta-analysis and analytical pipeline:The genes harboring variants in each patient were analyzed by gene set enrichment using the MSig database C2 collection of gene sets [43]. The significant gene sets for each patient were combined into a binary association matrix. The iBBiG algorithm extracts modules of gene sets and patient subsets from the data matrix. The modules are represented by different colors. Fisher’s exact test was used to identify modules with significant differences in the number of cases and controls.
Mentions: The genes containing variants that showed significant differences between cases and controls were examined for their association with networks and biological pathways using GSEA. We analyzed genes whose variants differed from controls with a p-value <0.1. We ran GSEA independently for each of the 48 patients. The significant gene sets from the GSEA of each patient were then compared by adapting a newly described meta-analytic approach known as iterative binary bi-clustering (iBBiG) [42]. The iBBiG algorithm identifies “modules” of gene sets and patient subsets from binary data [42]. Our analytical pipeline is illustrated in Fig 2.

Bottom Line: We compared variants identified by targeted sequencing of women with 2-3 generations of preterm birth with term controls without history of preterm birth.Additionally, SERPINB8, AZU1 and WASF3 showed significant differences in abundance of variants in the univariate comparison of cases and controls.The biological processes impacted by these gene sets included: cell motility, migration and locomotion; response to glucocorticoid stimulus; signal transduction; metabolic regulation and control of apoptosis.

View Article: PubMed Central - PubMed

Affiliation: Department of Pediatrics, Women & Infants Hospital of Rhode Island, Providence, Rhode Island, United States of America.

ABSTRACT
Understanding the genetic contribution(s) to the risk of preterm birth may lead to the development of interventions for treatment, prediction and prevention. Twin studies suggest heritability of preterm birth is 36-40%. Large epidemiological analyses support a primary maternal origin for recurrence of preterm birth, with little effect of paternal or fetal genetic factors. We exploited an "extreme phenotype" of preterm birth to leverage the likelihood of genetic discovery. We compared variants identified by targeted sequencing of women with 2-3 generations of preterm birth with term controls without history of preterm birth. We used a meta-genomic, bi-clustering algorithm to identify gene sets coordinately associated with preterm birth. We identified 33 genes including 217 variants from 5 modules that were significantly different between cases and controls. The most frequently identified and connected genes in the exome library were IGF1, ATM and IQGAP2. Likewise, SOS1, RAF1 and AKT3 were most frequent in the haplotype library. Additionally, SERPINB8, AZU1 and WASF3 showed significant differences in abundance of variants in the univariate comparison of cases and controls. The biological processes impacted by these gene sets included: cell motility, migration and locomotion; response to glucocorticoid stimulus; signal transduction; metabolic regulation and control of apoptosis.

No MeSH data available.


Related in: MedlinePlus