Polygenic Risk Modelling for Prediction of Epithelial Ovarian Cancer Risk

Funder: Funding details are provided in the Supplementary MaterialAbstractPolygenic risk scores (PRS) for epithelial ovarian cancer (EOC) have the potential to improve risk stratification. Joint estimation of Single Nucleotide Polymorphism (SNP) effects in models could improve predictive performance over standard approaches of PRS construction. Here, we implemented computationally-efficient, penalized, logistic regression models (lasso, elastic net, stepwise) to individual level genotype data and a Bayesian framework with continuous shrinkage, “select and shrink for summary statistics” (S4), to summary level data for epithelial non-mucinous ovarian cancer risk prediction. We developed the models in a dataset consisting of 23,564 non-mucinous EOC cases and 40,138 controls participating in the Ovarian Cancer Association Consortium (OCAC) and validated the best models in three populations of different ancestries: prospective data from 198,101 women of European ancestry; 7,669 women of East Asian ancestry; 1,072 women of African ancestry, and in 18,915 BRCA1 and 12,337 BRCA2 pathogenic variant carriers of European ancestry. In the external validation data, the model with the strongest association for non-mucinous EOC risk derived from the OCAC model development data was the S4 model (27,240 SNPs) with odds ratios (OR) of 1.38(95%CI:1.28–1.48,AUC:0.588) per unit standard deviation, in women of European ancestry; 1.14(95%CI:1.08–1.19,AUC:0.538) in women of East Asian ancestry; 1.38(95%CI:1.21-1.58,AUC:0.593) in women of African ancestry; hazard ratios of 1.37(95%CI:1.30–1.44,AUC:0.592) in BRCA1 pathogenic variant carriers and 1.51(95%CI:1.36-1.67,AUC:0.624) in BRCA2 pathogenic variant carriers. Incorporation of the S4 PRS in risk prediction models for ovarian cancer may have clinical utility in ovarian cancer prevention programs.</jats:p

Kepuasan kerja guru-guru aliran pendidikan teknikal dan vokasional di sekolah-sekolah menengah teknik di negeri Johor Darul Takzim

Kajian ini bertujuan untuk mengenalpasti tahap kepuasan kerja guru-guru teknikal dan vokasional di Sekolah-sekolah Menengah Teknik di Negeri Johor Darul Takzim dalam lima dimensi pekerjaan iaitu keadaan pekerjaan pada masa sekarang, gaji, peluang kenaikan pangkat, penyeliaan, dan rakan sekerja. Kajian ini juga dijalankan untuk mengenalpasti pengaruh faktor demografi guru-guru teknikal dan vokasional terhadap kepuasan kerja dalam lima dimensi pekerjaan. Selain itu, kajian ini juga bertujuan untuk mengenalpasti tahap kepentingan lima dimensi dalam pekerjaan mengikut susunan keutamaan. Sampel yang terlibat dalam kajian ini adalah seramai 326 orang guru-guru teknikal dan vokasional dari 10 buah Sekolah Menengah Teknik di Negeri Johor. Kajian ini dijalankan menerusi pendekatan kuantitatif di mana data dikumpul melalui soal selidik Job Descriptive Index (JDI). Darjah kebolehpercayaan bagi instrumen JDI yang digunakan ialah 0.8982. Secara keseluruhannya guru-guru teknikal dan vokasional adalah berada pada tahap kepuasan yang sederhana dengan capaian tahap kepuasan yang tinggi dalam tiga dimensi pekerjaan iaitu keadaan pekerjaan pada masa sekarang, penyeliaan, dan rakan sekerja. Bagi dimensi gaji dan peluang kenaikan pangkat pula, responden hanya berada pada tahap kepuasan sederhana sahaja. Hasil kajian juga menunjukkan kepuasan kerja guru-guru teknikal dan vokasional adalah dipengaruhi oleh faktor demografi mereka. Bagi susunan kepentingan lima dimensi dalam pekerjaan pula, dimensi peluang kenaikan pangkat dan gaji adalah amat dititikberatkan oleh guru-guru teknikal dan vokasional. Seterusnya diikuti dengan dimensi penyeliaan, keadaan pekerjaan pada masa sekarang, dan rakan sekerja yang merupakan dimensi-dimensi yang kurang dipentingkan oleh para guru teknikal dan vokasional

What Key Drivers Are Needed To Implement Material Efficiency Strategies? An Analysis Of The Electrical And Electronic Industry In Malaysia And Its Implications To Practitioners

A circular economy can be achieved by the efficient use of materials across different industries and sectors. In the manufacturing sectors, practicing material efficiency is one of the effective strategies to reduce material usage and solid waste generation. However, due to many unknown factors, such as key drivers to enhance material efficiency, most of the time, manufacturers are practicing at the minimum level of material saving. This study aims to examine the key drivers of material efficiency among electrical and electronic (E&E) companies to fulfill the aims of sustainable manufacturing. The data collection and synthesis were conducted using semi-structured interviews and an analytical hierarchy process survey. In this study, thirteen key drivers were found. Five internal drivers and eight external drivers with different priorities were found to influence E&E companies in the practice of material efficiency strategies. In addition, the drivers’ implications to different practitioner groups are suggested. To conclude, achieving material efficiency can be done effectively if the incentivized key drivers are clearly notified. This research is important to show the key drivers that influence the implementation of material efficiency strategies in the E&E industrie

The Value of Renal Biopsy in Non-Insulin-Dependent Diabetes Mellitus in Singapore over the Past Two Decades

10.1159/000505624Kidney Diseases64284–29

Polygenic risk modeling for prediction of epithelial ovarian cancer risk

Polygenic risk scores (PRS) for epithelial ovarian cancer (EOC) have the potential to improve risk stratification. Joint estimation of Single Nucleotide Polymorphism (SNP) effects in models could improve predictive performance over standard approaches of PRS construction. Here, we implemented computationally efficient, penalized, logistic regression models (lasso, elastic net, stepwise) to individual level genotype data and a Bayesian framework with continuous shrinkage, &quot;select and shrink for summary statistics&quot; (S4), to summary level data for epithelial non-mucinous ovarian cancer risk prediction. We developed the models in a dataset consisting of 23,564 non-mucinous EOC cases and 40,138 controls participating in the Ovarian Cancer Association Consortium (OCAC) and validated the best models in three populations of different ancestries: prospective data from 198,101 women of European ancestries; 7,669 women of East Asian ancestries; 1,072 women of African ancestries, and in 18,915 BRCA1 and 12,337 BRCA2 pathogenic variant carriers of European ancestries. In the external validation data, the model with the strongest association for non-mucinous EOC risk derived from the OCAC model development data was the S4 model (27,240 SNPs) with odds ratios (OR) of 1.38 (95% CI: 1.28-1.48, AUC: 0.588) per unit standard deviation, in women of European ancestries; 1.14 (95% CI: 1.08-1.19, AUC: 0.538) in women of East Asian ancestries; 1.38 (95% CI: 1.21-1.58, AUC: 0.593) in women of African ancestries; hazard ratios of 1.36 (95% CI: 1.29-1.43, AUC: 0.592) in BRCA1 pathogenic variant carriers and 1.49 (95% CI: 1.35-1.64, AUC: 0.624) in BRCA2 pathogenic variant carriers. Incorporation of the S4 PRS in risk prediction models for ovarian cancer may have clinical utility in ovarian cancer prevention programs.N

Polygenic risk modeling for prediction of epithelial ovarian cancer risk

10.1038/s41431-021-00987-7EUROPEAN JOURNAL OF HUMAN GENETICS303349-36

Polygenic risk modeling for prediction of epithelial ovarian cancer risk

Polygenic risk scores (PRS) for epithelial ovarian cancer (EOC) have the potential to improve risk stratification. Joint estimation of Single Nucleotide Polymorphism (SNP) effects in models could improve predictive performance over standard approaches of PRS construction. Here, we implemented computationally efficient, penalized, logistic regression models (lasso, elastic net, stepwise) to individual level genotype data and a Bayesian framework with continuous shrinkage, select and shrink for summary statistics (S4), to summary level data for epithelial non-mucinous ovarian cancer risk prediction. We developed the models in a dataset consisting of 23,564 non-mucinous EOC cases and 40,138 controls participating in the Ovarian Cancer Association Consortium (OCAC) and validated the best models in three populations of different ancestries: prospective data from 198,101 women of European ancestries; 7,669 women of East Asian ancestries; 1,072 women of African ancestries, and in 18,915 BRCA1 and 12,337 BRCA2 pathogenic variant carriers of European ancestries. In the external validation data, the model with the strongest association for non-mucinous EOC risk derived from the OCAC model development data was the S4 model (27,240 SNPs) with odds ratios (OR) of 1.38 (95% CI: 1.28-1.48, AUC: 0.588) per unit standard deviation, in women of European ancestries; 1.14 (95% CI: 1.08-1.19, AUC: 0.538) in women of East Asian ancestries; 1.38 (95% CI: 1.21-1.58, AUC: 0.593) in women of African ancestries; hazard ratios of 1.36 (95% CI: 1.29-1.43, AUC: 0.592) in BRCA1 pathogenic variant carriers and 1.49 (95% CI: 1.35-1.64, AUC: 0.624) in BRCA2 pathogenic variant carriers. Incorporation of the S4 PRS in risk prediction models for ovarian cancer may have clinical utility in ovarian cancer prevention programs

Correction: Polygenic risk modeling for prediction of epithelial ovarian cancer risk.

10.1038/s41431-022-01085-yEUROPEAN JOURNAL OF HUMAN GENETICS305630-63