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+<WRAP centeralign>##Data Science Seminar##\\ Hosted by the Department of Mathematics and Statistics</WRAP>
+  * Date: Tuesday, December 5, 2023
+  * Time: 12:00pm -- 1:00pm
+  * Room: Zoom
+  * Speaker: Dr. Xuexia Wang (Florida International University)
+  * Title: Genetic Association Test and Risk Prediction Modeling for Cardiomyopathy in Cancer Survivors.
+<WRAP center box 80%>
+<WRAP centeralign>**//Abstract//**</WRAP>
+ \\ This talk includes two recent studies. Study 1 is a methodology work: Gene-based association
+tests are widely used in Genome-wide Association Studies (GWAS). The power of a test is often limited by
+the sample size, the effect size, and the number of causal genetic variants or their directions in a gene. In addition, access to individual-level data is often limited. To resolve the existing limitations, we proposed an optimally weighted combination (OWC) test based on summary statistics from GWAS. We analytically
+proved that aggregating the variants in one gene is the same as using the weighted combination of Z-
+scores for each variant based on the proposed score test. Several popular methods are its special cases.
+We also numerically illustrated that the proposed test outperforms comparsion methods via simulation
+studies. Furthermore, we utilized schizophrenia GWAS data and fasting glucose GWAS meta-analysis data
+to demonstrate that our method outperforms comparsion methods in real data analyses. Study 2 is an
+application work: We used a carefully curated list of 87 previously published genetic variants to determine
+whether incorporation of genetic variants with non-genetic variables could improve identification of cancer
+survivors at risk for anthracycline-related cardiomyopathy. We used anthracycline-exposed childhood
+cancer survivors from a Children’s Oncology Group study (COG-ALTE03N1: 146 cases; 195 matched
+controls) as the discovery set. Replication was performed in two anthracycline-exposed survivor
+populations: i) childhood cancer survivors from the Childhood Cancer Survivor Study (CCSS: 126 cases;
+controls); ii) autologous blood or marrow transplantation (BMT) survivors from the BMT Survivor Study
+(BMTSS: 80 cases; 78 controls). The Clinical+Genetic Model performed better than the Clinical Model in
+COG-ALTE03N1 (AUC of Clinical+Genetic Model = 0.88 vs. AUC of Clinical Model = 0.81) and BMTSS (AUC
+of Clinical+Genetic Model = 0.72 vs. AUC of Clinical Model = 0.64), but not in CCSS (AUC of
+Clinical+Genetic Model = 0.88 vs. AUC of Clinical Model = 0.89). However, the Clinical+Genetic model
+performed marginally better in CCSS patients without cardiovascular risk ractors where cardiomyopathy
+developed within 30 years of anthracycline exposure (AUC of Clinical+Genetic Model = 0.90 vs. AUC of
+Clinical Model = 0.85). Conclusions: Adding a comprehensively assembled genetic profile to clinical
+characteristics improves identification of cancer survivors at risk for anthracycline-related cardiomyopathy.
+ \\
+\\
+Biography of the speaker: Dr. Wang is Professor of Biostatistics at the Florida International
+University and Professor (Adjunct) of Biostatistics in the School of Medicine at the University of Alabama at Birmingham. She has extensive experience in designing a study based on a specific research goal,
+providing protocol to collect sample data, performing quality control of a big dataset, supervising postdocs
+or graduate students in methods development and data analysis, interpreting the study findings. In the past
+years, she has worked with a number of principal investigators in grant applications by proposing study
+design, power estimation, methods, and procedures for data analysis. Dr. Wang has led data analysis for
+numerous projects in cancer etiology and treatment-related adverse outcomes ranging from candidate
+gene, genome-wide association study, to next-generation sequencing data analysis. In addition, she has
+developed many powerful statistical methods and computational tools in genetic association studies which
+contribute to the identification of the genetic susceptibility to complex diseases.
+</WRAP>