12 research outputs found
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EM-mosaic detects mosaic point mutations that contribute to congenital heart disease.
BackgroundThe contribution of somatic mosaicism, or genetic mutations arising after oocyte fertilization, to congenital heart disease (CHD) is not well understood. Further, the relationship between mosaicism in blood and cardiovascular tissue has not been determined.MethodsWe developed a new computational method, EM-mosaic (Expectation-Maximization-based detection of mosaicism), to analyze mosaicism in exome sequences derived primarily from blood DNA of 2530 CHD proband-parent trios. To optimize this method, we measured mosaic detection power as a function of sequencing depth. In parallel, we analyzed our cohort using MosaicHunter, a Bayesian genotyping algorithm-based mosaic detection tool, and compared the two methods. The accuracy of these mosaic variant detection algorithms was assessed using an independent resequencing method. We then applied both methods to detect mosaicism in cardiac tissue-derived exome sequences of 66 participants for which matched blood and heart tissue was available.ResultsEM-mosaic detected 326 mosaic mutations in blood and/or cardiac tissue DNA. Of the 309 detected in blood DNA, 85/97 (88%) tested were independently confirmed, while 7/17 (41%) candidates of 17 detected in cardiac tissue were confirmed. MosaicHunter detected an additional 64 mosaics, of which 23/46 (50%) among 58 candidates from blood and 4/6 (67%) of 6 candidates from cardiac tissue confirmed. Twenty-five mosaic variants altered CHD-risk genes, affecting 1% of our cohort. Of these 25, 22/22 candidates tested were confirmed. Variants predicted as damaging had higher variant allele fraction than benign variants, suggesting a role in CHD. The estimated true frequency of mosaic variants above 10% mosaicism was 0.14/person in blood and 0.21/person in cardiac tissue. Analysis of 66 individuals with matched cardiac tissue available revealed both tissue-specific and shared mosaicism, with shared mosaics generally having higher allele fraction.ConclusionsWe estimate that ~ 1% of CHD probands have a mosaic variant detectable in blood that could contribute to cardiac malformations, particularly those damaging variants with relatively higher allele fraction. Although blood is a readily available DNA source, cardiac tissues analyzed contributed ~ 5% of somatic mosaic variants identified, indicating the value of tissue mosaicism analyses
Robust identification of deletions in exome and genome sequence data based on clustering of Mendelian errors
Multiple tools have been developed to identify copy number variants (CNVs) from whole exome (WES) and whole genome sequencing (WGS) data. Current tools such as XHMM for WES and CNVnator for WGS identify CNVs based on changes in read depth. For WGS, other methods to identify CNVs include utilizing discordant read pairs and split reads and genome-wide local assembly with tools such as Lumpy and SvABA, respectively. Here, we introduce a new method to identify deletion CNVs from WES and WGS trio data based on the clustering of Mendelian errors (MEs). Using our Mendelian Error Method (MEM), we identified 127 deletions (inherited and de novo) in 2,601 WES trios from the Pediatric Cardiac Genomics Consortium, with a validation rate of 88% by digital droplet PCR. MEM identified additional de novo deletions compared with XHMM, and a significant enrichment of 15q11.2 deletions compared with controls. In addition, MEM identified eight cases of uniparental disomy, sample switches, and DNA contamination. We applied MEM to WGS data from the Genome In A Bottle Ashkenazi trio and identified deletions with 97% specificity. MEM provides a robust, computationally inexpensive method for identifying deletions, and an orthogonal approach for verifying deletions called by other tools
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EM-mosaic detects mosaic point mutations that contribute to congenital heart disease
Background
The contribution of somatic mosaicism, or genetic mutations arising after oocyte fertilization, to congenital heart disease (CHD) is not well understood. Further, the relationship between mosaicism in blood and cardiovascular tissue has not been determined.
Methods
We developed a new computational method, EM-mosaic (Expectation-Maximization-based detection of mosaicism), to analyze mosaicism in exome sequences derived primarily from blood DNA of 2530 CHD proband-parent trios. To optimize this method, we measured mosaic detection power as a function of sequencing depth. In parallel, we analyzed our cohort using MosaicHunter, a Bayesian genotyping algorithm-based mosaic detection tool, and compared the two methods. The accuracy of these mosaic variant detection algorithms was assessed using an independent resequencing method. We then applied both methods to detect mosaicism in cardiac tissue-derived exome sequences of 66 participants for which matched blood and heart tissue was available.
Results
EM-mosaic detected 326 mosaic mutations in blood and/or cardiac tissue DNA. Of the 309 detected in blood DNA, 85/97 (88%) tested were independently confirmed, while 7/17 (41%) candidates of 17 detected in cardiac tissue were confirmed. MosaicHunter detected an additional 64 mosaics, of which 23/46 (50%) among 58 candidates from blood and 4/6 (67%) of 6 candidates from cardiac tissue confirmed. Twenty-five mosaic variants altered CHD-risk genes, affecting 1% of our cohort. Of these 25, 22/22 candidates tested were confirmed. Variants predicted as damaging had higher variant allele fraction than benign variants, suggesting a role in CHD. The estimated true frequency of mosaic variants above 10% mosaicism was 0.14/person in blood and 0.21/person in cardiac tissue. Analysis of 66 individuals with matched cardiac tissue available revealed both tissue-specific and shared mosaicism, with shared mosaics generally having higher allele fraction.
Conclusions
We estimate that ~ 1% of CHD probands have a mosaic variant detectable in blood that could contribute to cardiac malformations, particularly those damaging variants with relatively higher allele fraction. Although blood is a readily available DNA source, cardiac tissues analyzed contributed ~ 5% of somatic mosaic variants identified, indicating the value of tissue mosaicism analyses
Robust identification of deletions in exome and genome sequence data based on clustering of Mendelian errors.
Recommended from our members
Robust identification of deletions in exome and genome sequence data based on clustering of Mendelian errors.
Multiple tools have been developed to identify copy number variants (CNVs) from whole exome (WES) and whole genome sequencing (WGS) data. Current tools such as XHMM for WES and CNVnator for WGS identify CNVs based on changes in read depth. For WGS, other methods to identify CNVs include utilizing discordant read pairs and split reads and genome-wide local assembly with tools such as Lumpy and SvABA, respectively. Here, we introduce a new method to identify deletion CNVs from WES and WGS trio data based on the clustering of Mendelian errors (MEs). Using our Mendelian Error Method (MEM), we identified 127 deletions (inherited and de novo) in 2,601 WES trios from the Pediatric Cardiac Genomics Consortium, with a validation rate of 88% by digital droplet PCR. MEM identified additional de novo deletions compared with XHMM, and a significant enrichment of 15q11.2 deletions compared with controls. In addition, MEM identified eight cases of uniparental disomy, sample switches, and DNA contamination. We applied MEM to WGS data from the Genome In A Bottle Ashkenazi trio and identified deletions with 97% specificity. MEM provides a robust, computationally inexpensive method for identifying deletions, and an orthogonal approach for verifying deletions called by other tools
Recommended from our members
Robust identification of deletions in exome and genome sequence data based on clustering of Mendelian errors
Multiple tools have been developed to identify copy number variants (CNVs) from whole exome (WES) and whole genome sequencing (WGS) data. Current tools such as XHMM for WES and CNVnator for WGS identify CNVs based on changes in read depth. For WGS, other methods to identify CNVs include utilizing discordant read pairs and split reads and genome-wide local assembly with tools such as Lumpy and SvABA, respectively. Here, we introduce a new method to identify deletion CNVs from WES and WGS trio data based on the clustering of Mendelian errors (MEs). Using our Mendelian Error Method (MEM), we identified 127 deletions (inherited and de novo) in 2,601 WES trios from the Pediatric Cardiac Genomics Consortium, with a validation rate of 88% by digital droplet PCR. MEM identified additional de novo deletions compared with XHMM, and a significant enrichment of 15q11.2 deletions compared with controls. In addition, MEM identified eight cases of uniparental disomy, sample switches, and DNA contamination. We applied MEM to WGS data from the Genome In A Bottle Ashkenazi trio and identified deletions with 97% specificity. MEM provides a robust, computationally inexpensive method for identifying deletions, and an orthogonal approach for verifying deletions called by other tools
Use of complementary and alternative medicine by mid-age women with back pain: a national crosssectional survey
Background: The use of complementary and alternative medicine (CAM) has increased significantly in Australia over the past decade. Back pain represents a common context for CAM use, with increasing utilisation of a wide range of therapies provided within and outside conventional medical facilities. We examine the relationship between back pain and use of CAM and conventional medicine in a national cohort of mid-aged Australian women.Methods: Data is taken from a cross-sectional survey (n = 10492) of the mid-aged cohort of the Australian Longitudinal Study on Women's Health, surveyed in 2007. The main outcome measures were: incidence of back pain the previous 12 months, and frequency of use of conventional or CAM treatments in the previous 12 months.Results: Back pain was experienced by 77% (n = 8063) of the cohort in the previous twelve month period. The majority of women with back pain only consulted with a conventional care provider (51.3%), 44.2% of women with back pain consulted with both a conventional care provider and a CAM practitioner. Women with more frequent back pain were more likely to consult a CAM practitioner, as well as seek conventional care. The most commonly utilised CAM practitioners were massage therapy (26.5% of those with back pain) and chiropractic (16.1% of those with back pain). Only 1.7% of women with back pain consulted with a CAM practitioner exclusively.Conclusions: Mid-aged women with back pain utilise a range of conventional and CAM treatments. Consultation with CAM practitioners or self-prescribed CAM was predominantly in addition to, rather than a replacement for, conventional care. It is important that health professionals are aware of potential multiple practitioner usage in the context of back pain and are prepared to discuss such behaviours and practices with their patients
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De Novo Damaging Variants, Clinical Phenotypes, and Post-Operative Outcomes in Congenital Heart Disease.
BackgroundDe novo genic and copy number variants are enriched in patients with congenital heart disease, particularly those with extra-cardiac anomalies. The impact of de novo damaging variants on outcomes following cardiac repair is unknown.MethodsWe studied 2517 patients with congenital heart disease who had undergone whole-exome sequencing as part of the CHD GENES study (Congenital Heart Disease Genetic Network).ResultsTwo hundred ninety-four patients (11.7%) had clinically significant de novo variants. Patients with de novo damaging variants were 2.4 times more likely to have extra-cardiac anomalies (P=5.63×10-12). In 1268 patients (50.4%) who had surgical data available and underwent open-heart surgery exclusive of heart transplantation as their first operation, we analyzed transplant-free survival following the first operation. Median follow-up was 2.65 years. De novo variants were associated with worse transplant-free survival (hazard ratio, 3.51; P=5.33×10-04) and longer times to final extubation (hazard ratio, 0.74; P=0.005). As de novo variants had a significant interaction with extra-cardiac anomalies for transplant-free survival (P=0.003), de novo variants conveyed no additional risk for transplant-free survival for patients with these anomalies (adjusted hazard ratio, 1.96; P=0.06). By contrast, de novo variants in patients without extra-cardiac anomalies were associated with worse transplant-free survival during follow-up (hazard ratio, 11.21; P=1.61×10-05) than that of patients with no de novo variants. Using agnostic machine-learning algorithms, we identified de novo copy number variants at 15q25.2 and 15q11.2 as being associated with worse transplant-free survival and 15q25.2, 22q11.21, and 3p25.2 as being associated with prolonged time to final extubation.ConclusionsIn patients with congenital heart disease undergoing open-heart surgery, de novo variants were associated with worse transplant-free survival and longer times on the ventilator. De novo variants were most strongly associated with adverse outcomes among patients without extra-cardiac anomalies, suggesting a benefit for preoperative genetic testing even when genetic abnormalities are not suspected during routine clinical practice. Registration: URL: https://www.clinicaltrials.gov. Unique identifier: NCT01196182
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De Novo Damaging Variants, Clinical Phenotypes, and Post-Operative Outcomes in Congenital Heart Disease.
BackgroundDe novo genic and copy number variants are enriched in patients with congenital heart disease, particularly those with extra-cardiac anomalies. The impact of de novo damaging variants on outcomes following cardiac repair is unknown.MethodsWe studied 2517 patients with congenital heart disease who had undergone whole-exome sequencing as part of the CHD GENES study (Congenital Heart Disease Genetic Network).ResultsTwo hundred ninety-four patients (11.7%) had clinically significant de novo variants. Patients with de novo damaging variants were 2.4 times more likely to have extra-cardiac anomalies (P=5.63×10-12). In 1268 patients (50.4%) who had surgical data available and underwent open-heart surgery exclusive of heart transplantation as their first operation, we analyzed transplant-free survival following the first operation. Median follow-up was 2.65 years. De novo variants were associated with worse transplant-free survival (hazard ratio, 3.51; P=5.33×10-04) and longer times to final extubation (hazard ratio, 0.74; P=0.005). As de novo variants had a significant interaction with extra-cardiac anomalies for transplant-free survival (P=0.003), de novo variants conveyed no additional risk for transplant-free survival for patients with these anomalies (adjusted hazard ratio, 1.96; P=0.06). By contrast, de novo variants in patients without extra-cardiac anomalies were associated with worse transplant-free survival during follow-up (hazard ratio, 11.21; P=1.61×10-05) than that of patients with no de novo variants. Using agnostic machine-learning algorithms, we identified de novo copy number variants at 15q25.2 and 15q11.2 as being associated with worse transplant-free survival and 15q25.2, 22q11.21, and 3p25.2 as being associated with prolonged time to final extubation.ConclusionsIn patients with congenital heart disease undergoing open-heart surgery, de novo variants were associated with worse transplant-free survival and longer times on the ventilator. De novo variants were most strongly associated with adverse outcomes among patients without extra-cardiac anomalies, suggesting a benefit for preoperative genetic testing even when genetic abnormalities are not suspected during routine clinical practice. Registration: URL: https://www.clinicaltrials.gov. Unique identifier: NCT01196182
De Novo Damaging Variants, Clinical Phenotypes, and Post-Operative Outcomes in Congenital Heart Disease
BackgroundDe novo genic and copy number variants are enriched in patients with congenital heart disease, particularly those with extra-cardiac anomalies. The impact of de novo damaging variants on outcomes following cardiac repair is unknown.MethodsWe studied 2517 patients with congenital heart disease who had undergone whole-exome sequencing as part of the CHD GENES study (Congenital Heart Disease Genetic Network).ResultsTwo hundred ninety-four patients (11.7%) had clinically significant de novo variants. Patients with de novo damaging variants were 2.4 times more likely to have extra-cardiac anomalies (P=5.63×10-12). In 1268 patients (50.4%) who had surgical data available and underwent open-heart surgery exclusive of heart transplantation as their first operation, we analyzed transplant-free survival following the first operation. Median follow-up was 2.65 years. De novo variants were associated with worse transplant-free survival (hazard ratio, 3.51; P=5.33×10-04) and longer times to final extubation (hazard ratio, 0.74; P=0.005). As de novo variants had a significant interaction with extra-cardiac anomalies for transplant-free survival (P=0.003), de novo variants conveyed no additional risk for transplant-free survival for patients with these anomalies (adjusted hazard ratio, 1.96; P=0.06). By contrast, de novo variants in patients without extra-cardiac anomalies were associated with worse transplant-free survival during follow-up (hazard ratio, 11.21; P=1.61×10-05) than that of patients with no de novo variants. Using agnostic machine-learning algorithms, we identified de novo copy number variants at 15q25.2 and 15q11.2 as being associated with worse transplant-free survival and 15q25.2, 22q11.21, and 3p25.2 as being associated with prolonged time to final extubation.ConclusionsIn patients with congenital heart disease undergoing open-heart surgery, de novo variants were associated with worse transplant-free survival and longer times on the ventilator. De novo variants were most strongly associated with adverse outcomes among patients without extra-cardiac anomalies, suggesting a benefit for preoperative genetic testing even when genetic abnormalities are not suspected during routine clinical practice. Registration: URL: https://www.clinicaltrials.gov. Unique identifier: NCT01196182