Friday, February 15 | 10:15 to 11:30 a.m. | THT 142 Vault 

Please join us for the Informatics Institute PowerTalk Seminar Series-Clinical Informatics with Linyong Mao, Ph.D., an assistant professor in the Department of Biochemistry and Molecular Biology at Howard University. The seminar is titled "Population Genomics and Gene Expression Data Analysis." This seminar will be a webinar through Zoom. Attendees may join us in the THT Vault for viewing or join from their own computer. Zoom meeting information can be found below.

Abstract: Things that will be discussed in this presentation:
• Structural variations (SVs), including deletions, insertions, inversions and duplications, constitute an important source of genetic diversity. I developed algorithms and a computational pipeline to discover and genotype structural variations in cucumber populations. I detected 26,788 SVs in 115 cucumber accessions. These SVs affect the coding regions of 1676 genes. The discovered SVs provide a landscape of structural variations in a plant and will serve as an important resource for exploring genes underlying key traits.
• Obesity is a global health problem. People of certain racial and ethnic groups are more (or less) likely to become obese. We hypothesized that the genetic factor may play a role in population disparities in the obesity prevalence. I collected more than 200 obesity-associated SNPs from GWAS Catalog, derived population-level allele frequencies from the genotype data of 1000 Genomes Project, and developed a statistical model to test the population differentiation in the effect allele frequencies. I also developed a composite genetic risk score to correlate genetic risks with obesity prevalence among worldwide populations. We have detected substantial population differentiation in allele frequencies of obesity-associated SNPs. In addition, the population-level average of composite genetic risk scores is significantly correlated (R2 = 0.35, P = 0.0060) with obesity prevalence.
• RNA-seq data analysis pipeline.
• Gene co-expression network and its functional modules

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Meeting ID: 491 849 069

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